katowice/introAI/Trees/Classification_trees.ipynb

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     "text": [
      "Collecting graphviz\n",
      "  Using cached https://files.pythonhosted.org/packages/1f/e2/ef2581b5b86625657afd32030f90cf2717456c1d2b711ba074bf007c0f1a/graphviz-0.10.1-py2.py3-none-any.whl\n",
      "Installing collected packages: graphviz\n",
      "Successfully installed graphviz-0.10.1\n"
     ]
    }
   ],
   "source": [
    "#!pip install --user graphviz \n",
    "#!apt-get install graphviz"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "xc7CY5WNJzhF",
    "lang": "pl"
   },
   "source": [
    "# Drzewa klasyfikacyjne (metoda rekurencyjnego podziału)\n",
    "\n",
    "Metoda ta polega na sekwencyjnym (rekurencyjnym) podziale przestrzeni danych na podprzestrzenie (hiperkostki, segmenty), aż zmienna zależna osiągnie w każdej podprzestrzeni minimalny poziom zróżnicowania."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "lang": "en"
   },
   "source": [
    "# Classification trees \n",
    "\n",
    "This method is based on the sequential (recursive) division of data space into subspaces (subsets) until the subset  has all the same values of the target variable"
   ]
  },
  {
   "cell_type": "markdown",
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   "source": [
    "\n",
    "## Wady i zalety drzewa decyzyjnego\n",
    "**Zalety:**\n",
    "\n",
    "1. *niezparametryczność* – przy budowaniu modelu nie zakłada się postaci rozkładów zmiennych ani relacji między nimi\n",
    "2. *adaptacyjność* – automatyczny proces doboru zmiennych do modelu, możliwość uwzględnieniem zarówno zmiennych jakościowych i ilościowych,\n",
    "3. *odporność na braki danych oraz występowanie wartości\n",
    "nietypowych* \n",
    "4. *łatwa interpretowalność* -  możliwość graficznego przedstawienia modelu w postaci drzewa, możliwość zapisania reguł klasyfikujących\n",
    "\n",
    "**Główna wada:**\n",
    "\n",
    "1. *niestabilność modelu* -  oznacza, że postać modelu silnie zależy od dostępnego zbioru uczącego, budowanie drzew na różnych podzbiorach trenujących daje bardzo różniące się modele"
   ]
  },
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   "cell_type": "markdown",
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    "lang": "en"
   },
   "source": [
    "## Advantages and disadvantages of a decision tree\n",
    "**Advantages:**\n",
    "\n",
    "1. *nonparametric* - no variable distributions or relationships between them are assumed  diuring building the model,\n",
    "2. *adaptability* - automatic process of selecting variables for the model, both qualitative and quantitative variables ca be taken into account,\n",
    "3. *resistance to missing values ​​ and occurrence of atypical values*\n",
    "4. *interpretable* - the model can be presented graphically as a tree and the classification rules can be saved\n",
    "\n",
    "**The main disadvantage:**\n",
    "\n",
    "1. *model instability* - means that the form of the model strongly depends on the available training set, building trees on different training subsets gives different models"
   ]
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   "source": [
    "## Algorytm tworzenia drzewa klasyfikującego\n",
    "\n",
    "**Kroki algorytmu**\n",
    "\n",
    "1. Mając treningory zbiór obiektów $S$, sprawdź, czy należą one do tej samej klasy. Jeżli\n",
    "tak, to zakończ pracę.\n",
    "2. W przeciwnym przypadku rozważ wszystkie możliwe podziały zbioru S na\n",
    "podzbiory $S_1$, $S_2$, ..., $S_m$ tak, aby były one jak najbardziej jednorodne.\n",
    "3. Dokonaj oceny jakości każdego z tych podziałów zgodnie z przyjętym kryterium\n",
    "i wybierz najlepszy z nich.\n",
    "4. Podziel zbiór $S$ w wybrany sposób.\n",
    "5. Wykonaj kroki 1-4 rekurencyjnie dla każdego z podzbiorów\n",
    "\n",
    "W domyślnej wersji algorytmu każdorazowo poszukiwany jest najlepszy podział pod względem zmiennej jak i sposobu podziału przestrzeni względem niej. To domyślne działanie można jednak zmienić, o czym powiemy za chwilę. \n",
    "\n",
    "\n",
    "## Kryterium podziału \n",
    "Domyślnie kryterium podziału jest minimalizacja zróżnicowania (heterogeniczności) elementów w każdym z podziałów. W powyższym przykładzie wykorzystywany jest wskaźnik Giniego.\n",
    "\n",
    "\n",
    "W pierwszej kolejności dla każdej z $K$ klas ($k \\in K$) dla każdego z $M$ podobszarów ($m \\in M$) wyznaczana jest średnia ilość elementów danej klasy w tym obszarze:\n",
    "\n",
    "$$p_{km} = \\frac{1}{N_s}\\sum_{x_i \\in S_m}I(y_i=k)$$\n",
    "\n",
    "$N_s$ to ilość elementów w danym obszarze.\n",
    "\n",
    "Współczynnik Giniego definiujemy jako:\n",
    "$$H(m) = \\sum_k p_{km}(1-p_{km})$$\n",
    "\n",
    "Gdy w danym obszerze występują tylko reprezentanci jednej klasy $p_{km}=1$ dla tej wybranej klasy. Dla pozostałych klas współczynnik ten wynosi 0. Wówczas $H=0$ dla takiego obszaru.\n"
   ]
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   "cell_type": "markdown",
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   "source": [
    "## Algorithm for creating a classification tree\n",
    "\n",
    "**Steps of the algorithm**\n",
    "\n",
    "1. Check if the objects in the training set $S$ belong to the same class. If yes, finish your work.\n",
    "2. Otherwise, consider all possible divisions of the set $S$ into\n",
    "subsets $S_1$, $S_2$, ..., $S_m$ so that they are as homogeneous as possible.\n",
    "3. Assess the quality of each division in accordance with the adopted criterion\n",
    "and choose the best one.\n",
    "4. Split the $S$ set in the chosen way.\n",
    "5. Perform steps 1-4 recursively for each subset.\n",
    "\n",
    "In the default version of the algorithm, the best division is always sought in terms of the variable and how the space is divided with respect to it. However, this default behavior can be changed, which we'll talk about in a moment.\n",
    "\n",
    "\n",
    "## Split point selection \n",
    "By default, the split should minimize the diversity (heterogeneity) of the elements in all subset. In the above example, the Gini index is used to measure heterogeneity.\n",
    "\n",
    "\n",
    "First, in all subareas $m \\in M$ the average number of elements of given class $k \\in K$ is determined:\n",
    "\n",
    "$$p_{km} = \\frac{1}{N_s}\\sum_{x_i \\in S_m}I(y_i=k)$$\n",
    "\n",
    "$N_s$ is the number of all elements in a given area.\n",
    "\n",
    "We define the Gini coefficient as:\n",
    "$$H(m) = \\sum_k p_{km}(1-p_{km})$$\n",
    "\n",
    "When in a given subarea $m$ there are only representatives of one class $k$ $p_{km}=1$. For other classes this coefficient is equal to 0. Then  Gini coefficient $H(m)=0$."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "lang": "pl"
   },
   "source": [
    "### Klasyfikacja z wykorzystaniem metody rekurencyjnego podziału dla danych $iris$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "lang": "en"
   },
   "source": [
    "### Tree classification of the $iris$ data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "xqxmdWvxImFk",
    "lang": "pl"
   },
   "source": [
    "W pierwszym kroku importujemy przydatne biblioteki. Drzewa klasyfikacyjne zostały zaimplementowane w bibliotece $sklearn$."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "lang": "en"
   },
   "source": [
    "Classification trees have been implemented in the $sklearn$ library, so in the first step, we import necessary functions from this library. "
   ]
  },
  {
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   "source": [
    "from sklearn.tree import DecisionTreeClassifier, export_graphviz\n",
    "#from sklearn import tree\n",
    "from sklearn.datasets import load_iris, load_breast_cancer\n",
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "lang": "pl"
   },
   "source": [
    "Bodatkowo biblioteka $graphviz$ będzie przydatna do wizualizacji otrzymanych drzew."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "lang": "en"
   },
   "source": [
    "In addition, the $graphviz$ library will be useful for visualizing the received trees."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
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    "colab": {},
    "colab_type": "code",
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   "source": [
    "import graphviz \n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "V4b3h5n59cUQ",
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   },
   "source": [
    "#### Zestaw danych IRIS"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "lang": "en"
   },
   "source": [
    "#### IRIS data set"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "Uby3ev8F_-pm",
    "lang": "pl"
   },
   "source": [
    "Informacje o kwiatech o polskiej nazwie **kosaciec**  dostępne w zestawie *Iris* zostały zebrane przez  biologa Ronalda Fishera w roku 1936. Wykorzystał on je w pracy  *The use of multiple measurements in taxonomic problems*.\n",
    "\n",
    "#### Iris Setosa\n",
    "![alt text](https://i.ebayimg.com/images/g/xEgAAOSwoBtW6BoO/s-l1600.jpg)\n",
    "#### Iris Virginica\n",
    "![alt text](https://upload.wikimedia.org/wikipedia/commons/thumb/f/f8/Iris_virginica_2.jpg/1280px-Iris_virginica_2.jpg)\n",
    "#### Iris Versicolor\n",
    "![alt text](http://latour-marliac.com/323-large_default/iris-versicolor-iris-versicolore.jpg)\n",
    "\n",
    "\n",
    "Dla reprezentantów poszczególnych  gatunków mierzono długość i szerokość płatka „petal” i działki kielicha kwiatu „sepal”."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "lang": "en"
   },
   "source": [
    "Information on iris flowers available in the set *Iris* was collected by biologist Ronald Fisher in 1936. He used it in his work *The use of multiple measurements in taxonomic problems*.\n",
    "\n",
    "####  Setosa Iris\n",
    "![alt text](https://i.ebayimg.com/images/g/xEgAAOSwoBtW6BoO/s-l1600.jpg)\n",
    "####  Virginica Iris\n",
    "![alt text](https://upload.wikimedia.org/wikipedia/commons/thumb/f/f8/Iris_virginica_2.jpg/1280px-Iris_virginica_2.jpg)\n",
    "####  Versicolor Iris\n",
    "![alt text](http://latour-marliac.com/323-large_default/iris-versicolor-iris-versicolore.jpg)\n",
    "\n",
    "\n",
    "For representatives of  particular species, the length and width of the petal and the sepal flower cup were measured."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "hb4cSOy4JO7r",
    "lang": "pl"
   },
   "source": [
    "**Krok 1:** Podziel dane na dane treningowe i testowe w proporcji 0.77 do 0.33. Ustaw `random_state` na 1."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "lang": "en"
   },
   "source": [
    "**Step 1:** Divide the data into training and test data in the ratio 0.77 to 0.33. Set `random_state` to 1."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "khhEhXUvjCZN"
   },
   "outputs": [],
   "source": [
    "iris = load_iris()\n",
    "X, y = iris.data, iris.target\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33, random_state=1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
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       "['sepal length (cm)',\n",
       " 'sepal width (cm)',\n",
       " 'petal length (cm)',\n",
       " 'petal width (cm)']"
      ]
     },
     "execution_count": 4,
     "metadata": {},
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   ],
   "source": [
    "iris.feature_names"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "VWet73ZiJZnL",
    "lang": "pl"
   },
   "source": [
    "**Krok 2:** Trening klasyfikatora  `DecisionTreeClassifier` z biblioteki `sklearn`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "lang": "en"
   },
   "source": [
    "**Step 2:** Training of the `DecisionTreeClassifier` classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 52
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    "colab_type": "code",
    "id": "UCEVM18f9Kry",
    "outputId": "c9768574-bcfe-4ba5-a188-f41cea3f9bae"
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     "output_type": "stream",
     "text": [
      "CPU times: user 0 ns, sys: 0 ns, total: 0 ns\n",
      "Wall time: 1.6 ms\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "clf = DecisionTreeClassifier()\n",
    "clf = clf.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "FLVcMn21Jm92",
    "lang": "pl"
   },
   "source": [
    "**Krok 2.1:** Podgląd otrzymanego drzewa decyzyjnego"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "lang": "en"
   },
   "source": [
    "**Step 2.1:** Preview of the received decision tree"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "lang": "pl"
   },
   "source": [
    "*wersja czarno-biała* "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "lang": "en"
   },
   "source": [
    "*black and white version*"
   ]
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  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
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    "colab_type": "code",
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   },
   "outputs": [],
   "source": [
    "dot_data = export_graphviz(clf, out_file=None) \n",
    "graph = graphviz.Source(dot_data) "
   ]
  },
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   "execution_count": 7,
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    "graph"
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  {
   "cell_type": "markdown",
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    "lang": "pl"
   },
   "source": [
    "*wersja kolorowa* "
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    "lang": "en"
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    "*color version*"
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    "id": "7AeTxVdCLmR4",
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      ],
      "text/plain": [
       "<graphviz.files.Source at 0x7f2babb545c0>"
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     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dot_data = export_graphviz(clf, out_file=None, \n",
    "                         feature_names=iris.feature_names,  \n",
    "                         class_names=iris.target_names,  \n",
    "                         filled=True, rounded=True,  \n",
    "                         special_characters=True)  \n",
    "graph = graphviz.Source(dot_data)  \n",
    "graph"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "lang": "pl"
   },
   "source": [
    "**Zadanie**\n",
    "\n",
    "Do jakiej klasy został przypożądkowany irys z zestawu testowego X_test[10]. Podaj nazwę."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "lang": "en"
   },
   "source": [
    "**Exercise**\n",
    "\n",
    "To which class belong the iris X_test[10] from the test set. Give the name."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'versicolor'"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris.target_names[clf.predict([X_test[10]])[0]]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "6EyAGPZfWPkr",
    "lang": "pl"
   },
   "source": [
    "## Dokładność modelu\n",
    "\n",
    "Metodą $score$ wyznacz dokładność otrzymanego modelu"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "lang": "en"
   },
   "source": [
    "## Model accuracy\n",
    "\n",
    "Using the $score$ method determine the accuracy of the obtained model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "PerPAAfLWPE4",
    "outputId": "4f999dce-d8f8-4148-d2c7-2363d0d8459f"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.96"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "nqdA5PT9WJon",
    "lang": "pl"
   },
   "source": [
    "## Podgląd otrzymanych podziałów\n",
    "\n",
    "Aby można było zwizualizować otrzymane podziały konieczna jest redukcja przestrzeni cech do dwóch wymirów. Oznacza to, że podczas klasyfikacji uwzględniamy tylko 2 zmienne objaśniające.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "lang": "en"
   },
   "source": [
    "## Preview of received divisions\n",
    "\n",
    "In order to visualize the obtained divisions, it is necessary to reduce the feature space to two dimensions. This means that we only include 2 explanatory variables during the classification.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
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    "colab": {
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    "colab_type": "code",
    "id": "oWVetjlilclA",
    "outputId": "9b74a8d2-17fe-450e-b7b4-a30f0653c9ca"
   },
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'plt' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-2-e9ce5e682052>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 6\u001b[0;31m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_cmap\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcm\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mjet\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      7\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      8\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'plt' is not defined"
     ]
    }
   ],
   "source": [
    "# Parameters\n",
    "n_classes = 3\n",
    "plot_step = 0.02\n",
    "\n",
    "\n",
    "plt.set_cmap(plt.cm.jet)\n",
    "\n",
    "\n",
    "for pairidx, pair in enumerate([[0, 1], [0, 2], [0, 3],\n",
    "                                [1, 2], [1, 3], [2, 3]]):\n",
    "  \n",
    "    # We train classifier only on 2 features\n",
    "    X = iris.data[:, pair]\n",
    "    y = iris.target\n",
    "\n",
    "    clf_tmp = DecisionTreeClassifier().fit(X, y)\n",
    "\n",
    "    # Plot\n",
    "    plt.subplot(2, 3, pairidx + 1)\n",
    "\n",
    "    x_min, x_max = X[:, 0].min() - 1, X[:, 0].max() + 1\n",
    "    y_min, y_max = X[:, 1].min() - 1, X[:, 1].max() + 1\n",
    "    xx, yy = np.meshgrid(np.arange(x_min, x_max, plot_step),\n",
    "                         np.arange(y_min, y_max, plot_step))\n",
    "\n",
    "    Z = clf_tmp.predict(np.c_[xx.ravel(), yy.ravel()])\n",
    "    Z = Z.reshape(xx.shape)\n",
    "    plt.imshow(Z,extent=[x_min,x_max,y_min,y_max],origin='lower',alpha=0.6)\n",
    "    plt.scatter(X[:,0], X[:,1],c=np.ravel(y),edgecolors='black')\n",
    "\n",
    "    plt.xlabel(iris.feature_names[pair[0]])\n",
    "    plt.ylabel(iris.feature_names[pair[1]])\n",
    "    plt.axis(\"tight\")\n",
    "\n",
    "\n",
    "    plt.tight_layout(pad=0.4, w_pad=0.5, h_pad=1.0)\n",
    "    plt.grid(False)\n",
    "\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "_fLDbVIUb_SY",
    "lang": "pl"
   },
   "source": [
    "## Inne opcje poszukiwania najlepszego podziału\n",
    "\n",
    "Kiedy mamy zbyt dużo danych lub zmiennych objaśniających czas poszukiwania optymalnego podziału rośnie. Aby w każdym kroku nie przeszukiwać wszystkich możliwych podziałów mamy dwie możliwości. Możliwe ustawienia:\n",
    "\n",
    "### `max_features`\n",
    "\n",
    "Ilość zmiennych uwzględnianych w czasie poszukiwania optymalnego podziału. Możliwe opcje:\n",
    "*  liczba całkowita - uwzględnia dokładnie tyle ziennych podczas każdego podziału\n",
    "*  liczba zmiennoprzecinkowa - tyle procent zmiennych będzie uwzględnianych\n",
    "*  “auto” -  wówczas `max_features=sqrt(n_features)`.\n",
    "*  “sqrt” - wówczas `max_features=sqrt(n_features)`\n",
    "*  “log2” - wówczas `max_features=log2(n_features)`.\n",
    "*  `None` - wówczas `max_features=n_features`.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "lang": "en"
   },
   "source": [
    "## Other split point search options\n",
    "\n",
    "When we have too much data or explanatory variables, the time to search for the optimal division increases. To avoid searching all possible divisions at every step, we have two options. Possible settings:\n",
    "\n",
    "### `max _features`\n",
    "\n",
    "The number of variables taken into account when searching for the optimal division. Possible options:\n",
    "* integer - includes exactly the number of variables during each division\n",
    "* floating point number - this is the percentage of variables that will be included\n",
    "* \"Auto\" - then `max_ features = sqrt (n _features)`.\n",
    "* \"Sqrt\" - then `max_ features = sqrt (n _features)`\n",
    "* \"Log2\" - then `max_ features = log2 (n _features)`.\n",
    "* `None` - then` max_ features = n_features`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.96 1.0\n",
      "0.88 1.0\n"
     ]
    }
   ],
   "source": [
    "clf_all = DecisionTreeClassifier()\n",
    "clf_all = clf_all.fit(X_train, y_train)\n",
    "\n",
    "print(clf_all.score(X_test, y_test), clf_all.score(X_train, y_train))\n",
    "\n",
    "\n",
    "clf = DecisionTreeClassifier(max_features=1, random_state=123)\n",
    "clf = clf.fit(X_train, y_train)\n",
    "\n",
    "print(clf.score(X_test, y_test), clf.score(X_train, y_train))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "-LAzXmCRWfMx",
    "lang": "pl"
   },
   "source": [
    "### `splitter='random'`:\n",
    "\n",
    "W tym przypadku wybór zmiennej względem której dokonywany jest podział będzie losowy."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "lang": "en"
   },
   "source": [
    "### `splitter = 'random'`:\n",
    "\n",
    "In this case, the selection of the variable in relation to which the division is made will be random."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 51
    },
    "colab_type": "code",
    "id": "mDhFHQHoWeni",
    "outputId": "fb2900e9-b5c9-4182-c023-1708da3a786e"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 0 ns, sys: 0 ns, total: 0 ns\n",
      "Wall time: 1.29 ms\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "clf = DecisionTreeClassifier(splitter='random', random_state=123)\n",
    "clf = clf.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "sDhRFST-YQfF",
    "outputId": "254ba679-c7d5-46f8-e684-b7d698f4d2ab"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.94, 1.0)"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf.score(X_test, y_test), clf.score(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "lang": "pl"
   },
   "source": [
    "### Powównanie 2 podziałów dla różnej inicjalizacji generatora liczb pseudolosowych"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "lang": "en"
   },
   "source": [
    "### Comparison of the 2 divisions for different initialization of the random number generator"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 434
    },
    "colab_type": "code",
    "id": "mduLh5B4nwKe",
    "outputId": "af741af5-00e8-4f88-e0dc-b1c037b84f0f"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "No handles with labels found to put in legend.\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "# Parameters\n",
    "n_classes = 3\n",
    "plot_step = 0.02\n",
    "\n",
    "plt.set_cmap(plt.cm.jet)\n",
    "\n",
    "\n",
    "for pairidx, pair in enumerate([[0, 1], [0, 2], [0, 3],\n",
    "                                [1, 2], [1, 3], [2, 3]]):\n",
    "    X = iris.data[:, pair]\n",
    "    y = iris.target\n",
    "\n",
    "\n",
    "    clf_tmp = DecisionTreeClassifier(splitter='random', random_state=2).fit(X, y)\n",
    "\n",
    "    plt.subplot(2, 3, pairidx + 1)\n",
    "\n",
    "    x_min, x_max = X[:, 0].min() - 1, X[:, 0].max() + 1\n",
    "    y_min, y_max = X[:, 1].min() - 1, X[:, 1].max() + 1\n",
    "    xx, yy = np.meshgrid(np.arange(x_min, x_max, plot_step),\n",
    "                         np.arange(y_min, y_max, plot_step))\n",
    "\n",
    "    Z = clf_tmp.predict(np.c_[xx.ravel(), yy.ravel()])\n",
    "    Z = Z.reshape(xx.shape)\n",
    "    plt.imshow(Z,extent=[x_min,x_max,y_min,y_max],origin='lower',alpha=0.6)\n",
    "    plt.scatter(X[:,0], X[:,1],c=np.ravel(y),edgecolors='black')\n",
    "\n",
    "    plt.xlabel(iris.feature_names[pair[0]])\n",
    "    plt.ylabel(iris.feature_names[pair[1]])\n",
    "    plt.axis(\"tight\")\n",
    "\n",
    "    plt.tight_layout(pad=0.4, w_pad=0.5, h_pad=1.0)\n",
    "    plt.grid(False)\n",
    "\n",
    "\n",
    "plt.legend()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "No handles with labels found to put in legend.\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "# Parameters\n",
    "n_classes = 3\n",
    "plot_step = 0.02\n",
    "\n",
    "plt.set_cmap(plt.cm.jet)\n",
    "\n",
    "\n",
    "for pairidx, pair in enumerate([[0, 1], [0, 2], [0, 3],\n",
    "                                [1, 2], [1, 3], [2, 3]]):\n",
    "    X = iris.data[:, pair]\n",
    "    y = iris.target\n",
    "\n",
    "\n",
    "    clf_tmp = DecisionTreeClassifier(splitter='random', random_state=100).fit(X, y)\n",
    "\n",
    "    plt.subplot(2, 3, pairidx + 1)\n",
    "\n",
    "    x_min, x_max = X[:, 0].min() - 1, X[:, 0].max() + 1\n",
    "    y_min, y_max = X[:, 1].min() - 1, X[:, 1].max() + 1\n",
    "    xx, yy = np.meshgrid(np.arange(x_min, x_max, plot_step),\n",
    "                         np.arange(y_min, y_max, plot_step))\n",
    "\n",
    "    Z = clf_tmp.predict(np.c_[xx.ravel(), yy.ravel()])\n",
    "    Z = Z.reshape(xx.shape)\n",
    "    plt.imshow(Z,extent=[x_min,x_max,y_min,y_max],origin='lower',alpha=0.6)\n",
    "    plt.scatter(X[:,0], X[:,1],c=np.ravel(y),edgecolors='black')\n",
    "\n",
    "    plt.xlabel(iris.feature_names[pair[0]])\n",
    "    plt.ylabel(iris.feature_names[pair[1]])\n",
    "    plt.axis(\"tight\")\n",
    "\n",
    "    plt.tight_layout(pad=0.4, w_pad=0.5, h_pad=1.0)\n",
    "    plt.grid(False)\n",
    "\n",
    "\n",
    "plt.legend()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "ueHn39W-ZU--",
    "lang": "pl"
   },
   "source": [
    "## Przeciwdziałanie przeuczeniu\n",
    "\n",
    "Wymuszanie jednorodności w otrzymanych podprzestrzeniach prowadzi na ogół do zbudowaniabardzo złożonego modelu, który jest nadmiernie dopasowany dodanych ze zbioru uczącego. Pojawia się wówczas efekt przeuczenia.\n",
    "\n",
    "Następujące parametry pozwalają zmniejszyć efekt *overfittingu*. Po znaku równości zostały podane ich domyślne wartości.\n",
    "\n",
    "*   `max_depth=None`\n",
    "*   `min_samples_split=2`\n",
    "*   `min_samples_leaf=1`\n",
    "*  `min_impurity_decrease=0`\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "lang": "en"
   },
   "source": [
    "## Preventing overfitting\n",
    "\n",
    "Forcing homogeneity in the obtained subspaces usually leads to the construction of a very complex model, which is overfitted to the learning set. \n",
    "\n",
    "The following parameters reduce the effect of the *overfitting*. After the equal sign their default values are given.\n",
    "\n",
    "* `max_depth = None`\n",
    "* `min _samples_ split = 2`\n",
    "* `min_samples_leaf = 1`\n",
    "* `min _impurity_ decrease = 0`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 51
    },
    "colab_type": "code",
    "id": "KujHLNMceskO",
    "outputId": "5941d90c-84e5-47ed-f6c8-abf2cd71b669"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 0 ns, sys: 0 ns, total: 0 ns\n",
      "Wall time: 1.41 ms\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "clf = DecisionTreeClassifier(min_samples_split=10)\n",
    "clf = clf.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "9gFDcJzhez9K"
   },
   "outputs": [],
   "source": [
    "dot_data = export_graphviz(clf, out_file=None) \n",
    "graph = graphviz.Source(dot_data) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 598
    },
    "colab_type": "code",
    "id": "FKMiEYyVe6EC",
    "outputId": "409f40a5-0402-41e5-fc3d-04e1cbd4aca6"
   },
   "outputs": [
    {
     "data": {
      "image/svg+xml": [
       "<?xml version=\"1.0\" encoding=\"UTF-8\" standalone=\"no\"?>\n",
       "<!DOCTYPE svg PUBLIC \"-//W3C//DTD SVG 1.1//EN\"\n",
       " \"http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd\">\n",
       "<!-- Generated by graphviz version 2.38.0 (20140413.2041)\n",
       " -->\n",
       "<!-- Title: Tree Pages: 1 -->\n",
       "<svg width=\"547pt\" height=\"433pt\"\n",
       " viewBox=\"0.00 0.00 547.00 433.00\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">\n",
       "<g id=\"graph0\" class=\"graph\" transform=\"scale(1 1) rotate(0) translate(4 429)\">\n",
       "<title>Tree</title>\n",
       "<polygon fill=\"white\" stroke=\"none\" points=\"-4,4 -4,-429 543,-429 543,4 -4,4\"/>\n",
       "<!-- 0 -->\n",
       "<g id=\"node1\" class=\"node\"><title>0</title>\n",
       "<path fill=\"#8139e5\" fill-opacity=\"0.043137\" stroke=\"black\" d=\"M256,-425C256,-425 133,-425 133,-425 127,-425 121,-419 121,-413 121,-413 121,-354 121,-354 121,-348 127,-342 133,-342 133,-342 256,-342 256,-342 262,-342 268,-348 268,-354 268,-354 268,-413 268,-413 268,-419 262,-425 256,-425\"/>\n",
       "<text text-anchor=\"start\" x=\"129\" y=\"-409.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">petal width (cm) ≤ 0.8</text>\n",
       "<text text-anchor=\"start\" x=\"159\" y=\"-394.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.665</text>\n",
       "<text text-anchor=\"start\" x=\"149.5\" y=\"-379.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 100</text>\n",
       "<text text-anchor=\"start\" x=\"136.5\" y=\"-364.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [33, 31, 36]</text>\n",
       "<text text-anchor=\"start\" x=\"146\" y=\"-349.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">class = virginica</text>\n",
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     },
     "execution_count": 74,
     "metadata": {},
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   ],
   "source": [
    "dot_data = export_graphviz(clf, out_file=None, \n",
    "                         feature_names=iris.feature_names,  \n",
    "                         class_names=iris.target_names,  \n",
    "                         filled=True, rounded=True,  \n",
    "                         special_characters=True)  \n",
    "graph = graphviz.Source(dot_data)  \n",
    "graph"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "AEZ3E2CLfnka",
    "outputId": "af4cc3b0-6f98-42de-8ae4-1e563d074992"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.96, 0.98)"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf.score(X_test, y_test), clf.score(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "u7lmH3w1dyLQ",
    "lang": "pl"
   },
   "source": [
    "## Poszukiwanie optymalnych parametrów metodą walidacji krzyżowej\n",
    "\n",
    "**Zapamiętaj:** danych testowych możesz użyć tylko raz do sprawdzenia dokładności ostatecznego modelu. \n",
    "\n",
    "Aby dopasować parametry drzewa klasyfikacyjnego można wykorzystać oddzielny zbiór walidacyjny lub **metodę walidacji krzyżowej**. W tym prykładzie użyjemy tej drugiej metody.\n",
    "\n",
    "Metoda ta pozwala określić dokładność modelu w sposób nieobciążony. \n",
    "Algorytm metody sprawdzania krzyżowego:\n",
    "\n",
    "1. Wybierz wartość parametru $b$ (zazwyczaj $b\\leq 10$) i podziel losowo zbiór uczący $U$ na $b$ równolicznych (w przybliżeniu) i rozłącznych części $U_1$, $U_2$, . . . , $U_b$.\n",
    "\n",
    "2.  Dla $h \\in {1, . . . , b}$ wykonaj kroki:\n",
    "\n",
    "  a. zbuduj model na zbiorze $U|U_h$ (wyłączając jedną część),\n",
    "\n",
    "  b.  oszacuj błąd klasyfikacji modelu na zbiorze $U_h$ (wykorzystując $U_h$ jako zbiór walidacyjny).\n",
    "\n",
    "3. Wyznacz ocenę błędu klasyfikacji modelu uśredniając wartości otrzymane w kroku 2. Otrzymany wynik jest nieobciążonym estymatorem błędu klasyfikacji\n",
    "\n",
    "Biiblioteka `sklearn` dostarcza metodę `GridSearchCV` do przeszukiwania przestrzeni hiperparametrów modelu z wykorzystaniem walidacji krzyżowej.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "lang": "en"
   },
   "source": [
    "## Searching for optimal parameters by cross-validation\n",
    "\n",
    "**Remember:** test data can only be used once to check the accuracy of the final model.\n",
    "\n",
    "To adjust the classification tree parameters, you can use a separate validation data set or **cross-validation method**. In this projection we will use the second method.\n",
    "\n",
    "Cross-validation method allows you to determine the accuracy of the model in an unbiased way.\n",
    "Algorithm of the cross-validation method:\n",
    "\n",
    "1. Select the value of the $b$ parameter (usually $b\\leq 10$) and randomly divide the training set $U$ into $b$ equal (approximately) and disjoint parts  $U_1$, $U_2$,. . . , $U_b$.\n",
    "\n",
    "2. For $h \\in {1, . . . , b}$, follow these steps:\n",
    "\n",
    "  a. build a model on the $U|U_h$ set (excluding one part),\n",
    "\n",
    "  b. estimate the model classification error on the $U_h$ set (using $U_h$ as the validation set).\n",
    "\n",
    "3. Calculate the classification error of the model by averaging the values obtained in step 2. The obtained result is an unbiased estimator of the classification error.\n",
    "\n",
    "The `sklearn` library provides the` GridSearchCV` method for searching the hyperparameter space of a model using cross-validation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 51
    },
    "colab_type": "code",
    "id": "VGs0zVx4dxwj",
    "outputId": "0fb92a80-df72-4240-a757-8d4d454dce86"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Najlepsza dokładność 0.94\n",
      "Dobrane parametry: {'max_depth': 3, 'max_features': 4}\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import GridSearchCV\n",
    "\n",
    "parameter_grid = {'max_depth': np.arange(3, 7),\n",
    "                  'max_features': np.arange(1, 5)}\n",
    "\n",
    "clf_grid = GridSearchCV(DecisionTreeClassifier(), parameter_grid)\n",
    "\n",
    "clf_grid.fit(X_train, y_train)\n",
    "\n",
    "print (\"Najlepsza dokładność {}\".format(clf_grid.best_score_))\n",
    "print (\"Dobrane parametry: {}\".format(clf_grid.best_params_))\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "ZznbsWO7Y0Es",
    "lang": "pl"
   },
   "source": [
    "Teraz możemy przetestować otrzymany model na danych testowych"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "lang": "en"
   },
   "source": [
    "Now we can test the received model on test data set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "D0c7HoLqdx2M",
    "outputId": "d89020ca-e3bd-401f-ca7e-fa47ae0d7bae"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.96"
      ]
     },
     "execution_count": 18,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf_grid.best_estimator_.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "OUSPUTNNeLBB",
    "lang": "pl"
   },
   "source": [
    "# Istotność zmiennych objaśniających\n",
    "\n",
    "Zaletą drzewa klasyfikacyjnego jest możliwość określenia istotności poszczególnych zmiennych"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "lang": "en"
   },
   "source": [
    "# Significance of explanatory variables\n",
    "\n",
    "The advantage of a classification tree is the ability to determine the significance of individual variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 85
    },
    "colab_type": "code",
    "id": "u3JoUJoOZKFT",
    "outputId": "8fb961a6-1693-4cd1-e9db-e02c24eec3d8"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "petal width (cm) 0.9308196108729353\n",
      "petal length (cm) 0.06918038912706467\n",
      "sepal width (cm) 0.0\n",
      "sepal length (cm) 0.0\n"
     ]
    }
   ],
   "source": [
    "ids = np.argsort(clf_grid.best_estimator_.feature_importances_)\n",
    "\n",
    "for i in ids[::-1]:\n",
    "    print (iris.feature_names[i], clf_grid.best_estimator_.feature_importances_[i])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "uEx_wwova6ag",
    "lang": "pl"
   },
   "source": [
    "Dla poprzedniego modelu"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "lang": "en"
   },
   "source": [
    "For the previous model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "eNnGI_rwd-U6"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 85
    },
    "colab_type": "code",
    "id": "VwYBSykLZwn6",
    "outputId": "7cf1285a-f1df-40ca-98b7-46d6f95a913f"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "petal length (cm) 0.592104014625634\n",
      "petal width (cm) 0.407895985374366\n",
      "sepal width (cm) 0.0\n",
      "sepal length (cm) 0.0\n"
     ]
    }
   ],
   "source": [
    "ids = np.argsort(clf.feature_importances_)\n",
    "\n",
    "\n",
    "for i in ids[::-1]:\n",
    "    print (iris.feature_names[i], clf.feature_importances_[i])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "t1Jdan2EYy1a",
    "lang": "pl"
   },
   "source": [
    "# Zadanie\n",
    "\n",
    "Zbuduj drzewo klasyfikacyjne dla danych dotyczących złośliwości raka piersi. Znajdź optymalne parametry i sprawdź jaka jest istotność poszczególnych zmiennych objaśniających.\n",
    "\n",
    "**Krok 1:** Wczytanie danych i podział na dane treningowe  i testowe w proporcji $0.77:0.33$. Aby móc porównać wyniki z kolegami ustaw `random_state` na 1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "lang": "en"
   },
   "source": [
    "# Exercise\n",
    "\n",
    "Build a classification tree for breast cancer malignancy data. Find the optimal parameters and check the significance of each explanatory variable.\n",
    "\n",
    "**Step 1:** Load data and divide them into training and test data in the $0.77:0.33$ ratio. To be able to compare results with colleagues, set `random_state` to 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "9geKa26u6MK_"
   },
   "outputs": [],
   "source": [
    "breast = load_breast_cancer()\n",
    "\n",
    "### Your solution\n",
    "#raise NotImplementedError\n",
    "\n",
    "X, y = breast.data, breast.target\n",
    "X_train_breast, X_test_breast, y_train_breast, y_test_breast = train_test_split(X, y, test_size=0.33, random_state=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "e27iUUW4cX9I",
    "lang": "pl"
   },
   "source": [
    "**Krok 2:** Medodą walidacji krzyżowej znajdź optymalne drzewo klasyfikujące:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "lang": "en"
   },
   "source": [
    "**Step 2:** Use the cross-validation method to find the optimal classification tree:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 51
    },
    "colab_type": "code",
    "id": "I2He_LUZRavR",
    "outputId": "7ebe9cca-a80f-42d6-9874-88431adbc16f"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Najlepsza dokładność 0.9606299212598425\n",
      "Dobrane parametry: {'max_depth': 20, 'max_features': 13}\n"
     ]
    }
   ],
   "source": [
    "parameter_grid = {'max_depth': np.arange(3, 30),\n",
    "                  'max_features': np.arange(1, 30)}\n",
    "\n",
    "### Your solution\n",
    "#raise NotImplementedError\n",
    "\n",
    "\n",
    "clf_grid = GridSearchCV(DecisionTreeClassifier(), parameter_grid)\n",
    "\n",
    "clf_grid.fit(X_train_breast, y_train_breast)\n",
    "\n",
    "print (\"Best accurancy {}\".format(clf_grid.best_score_))\n",
    "print (\"Best parametrs: {}\".format(clf_grid.best_params_))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "8landUcadIep",
    "lang": "pl"
   },
   "source": [
    "**Krok 3:** Podaj dokładność modelu na danych testowych:\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "lang": "en"
   },
   "source": [
    "**Step 3:** Calculate the accuracy of the model on the test data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "bX-bWj9yo_ui",
    "outputId": "024fa03f-a43f-465d-9cb5-16dcf3d2fae6"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.925531914893617"
      ]
     },
     "execution_count": 23,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### Your solution\n",
    "#raise NotImplementedError\n",
    "clf_grid.best_estimator_.score(X_test_breast, y_test_breast)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "rTmCCQsidWX4",
    "lang": "pl"
   },
   "source": [
    "**Krok 4:** Sprawdź istotność poszczególnych zmiennych"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "lang": "en"
   },
   "source": [
    "**Step 4:** Check the significance of individual variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 527
    },
    "colab_type": "code",
    "id": "37EZmHHxdAZL",
    "outputId": "5d4703d5-2d9b-4de8-b0d9-ef3ce4b164ea"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mean concave points 0.6728849843685007\n",
      "worst perimeter 0.13845715403142805\n",
      "worst radius 0.06696369390246942\n",
      "worst texture 0.043571915945865534\n",
      "mean area 0.025844525844525842\n",
      "worst smoothness 0.010515637098934591\n",
      "mean texture 0.010460879508498555\n",
      "worst concave points 0.009359734297077649\n",
      "mean smoothness 0.005538112680969823\n",
      "texture error 0.005538112680969823\n",
      "area error 0.005484861597498917\n",
      "worst compactness 0.005380388043260988\n",
      "radius error 0.0\n",
      "mean perimeter 0.0\n",
      "mean compactness 0.0\n",
      "mean concavity 0.0\n",
      "mean symmetry 0.0\n",
      "mean fractal dimension 0.0\n",
      "worst fractal dimension 0.0\n",
      "smoothness error 0.0\n",
      "perimeter error 0.0\n",
      "worst symmetry 0.0\n",
      "compactness error 0.0\n",
      "concavity error 0.0\n",
      "concave points error 0.0\n",
      "symmetry error 0.0\n",
      "fractal dimension error 0.0\n",
      "worst area 0.0\n",
      "worst concavity 0.0\n",
      "mean radius 0.0\n"
     ]
    }
   ],
   "source": [
    "### Your solution\n",
    "#raise NotImplementedError\n",
    "\n",
    "ids = np.argsort(clf_grid.best_estimator_.feature_importances_)\n",
    "\n",
    "for i in ids[::-1]:\n",
    "    print (breast.feature_names[i], clf_grid.best_estimator_.feature_importances_[i])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "RqADYKR-gEhF",
    "lang": "pl"
   },
   "source": [
    "**Krok 5:** Podgląd otrzymanego drzewa"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "lang": "en"
   },
   "source": [
    "**Step 5:** Preview of the received tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1094
    },
    "colab_type": "code",
    "id": "bFLFeyudgEsf",
    "outputId": "e37701fa-3873-4e0f-8165-813c8f7499b0"
   },
   "outputs": [
    {
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       "<text text-anchor=\"start\" x=\"1215.5\" y=\"-766.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">worst concave points ≤ 0.142</text>\n",
       "<text text-anchor=\"start\" x=\"1278.5\" y=\"-751.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.474</text>\n",
       "<text text-anchor=\"start\" x=\"1268\" y=\"-736.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 381</text>\n",
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       "<text text-anchor=\"start\" x=\"1003.5\" y=\"-647.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">worst radius ≤ 16.8</text>\n",
       "<text text-anchor=\"start\" x=\"1030.5\" y=\"-632.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.155</text>\n",
       "<text text-anchor=\"start\" x=\"1020\" y=\"-617.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 248</text>\n",
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       "<text text-anchor=\"start\" x=\"1457\" y=\"-647.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">fractal dimension error ≤ 0.013</text>\n",
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       "<text text-anchor=\"start\" x=\"520.5\" y=\"-528.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">worst concave points ≤ 0.136</text>\n",
       "<text text-anchor=\"start\" x=\"583.5\" y=\"-513.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.051</text>\n",
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       "<text text-anchor=\"start\" x=\"984\" y=\"-528.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">mean symmetry ≤ 0.151</text>\n",
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       "<text text-anchor=\"start\" x=\"266.5\" y=\"-409.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">area error ≤ 38.35</text>\n",
       "<text text-anchor=\"start\" x=\"290.5\" y=\"-394.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.018</text>\n",
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       "</g>\n",
       "<!-- 2&#45;&gt;10 -->\n",
       "<g id=\"edge10\" class=\"edge\"><title>2&#45;&gt;10</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M627.5,-460.907C627.5,-452.649 627.5,-443.864 627.5,-435.302\"/>\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"631,-435.021 627.5,-425.021 624,-435.021 631,-435.021\"/>\n",
       "</g>\n",
       "<!-- 4 -->\n",
       "<g id=\"node5\" class=\"node\"><title>4</title>\n",
       "<path fill=\"#399de5\" stroke=\"black\" d=\"M189.5,-298.5C189.5,-298.5 81.5,-298.5 81.5,-298.5 75.5,-298.5 69.5,-292.5 69.5,-286.5 69.5,-286.5 69.5,-242.5 69.5,-242.5 69.5,-236.5 75.5,-230.5 81.5,-230.5 81.5,-230.5 189.5,-230.5 189.5,-230.5 195.5,-230.5 201.5,-236.5 201.5,-242.5 201.5,-242.5 201.5,-286.5 201.5,-286.5 201.5,-292.5 195.5,-298.5 189.5,-298.5\"/>\n",
       "<text text-anchor=\"start\" x=\"100.5\" y=\"-283.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.0</text>\n",
       "<text text-anchor=\"start\" x=\"81\" y=\"-268.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 211</text>\n",
       "<text text-anchor=\"start\" x=\"77.5\" y=\"-253.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [0, 211]</text>\n",
       "<text text-anchor=\"start\" x=\"82.5\" y=\"-238.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">class = benign</text>\n",
       "</g>\n",
       "<!-- 3&#45;&gt;4 -->\n",
       "<g id=\"edge4\" class=\"edge\"><title>3&#45;&gt;4</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M265.458,-341.907C244.438,-329.549 221.41,-316.01 200.655,-303.807\"/>\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"202.307,-300.718 191.912,-298.667 198.759,-306.752 202.307,-300.718\"/>\n",
       "</g>\n",
       "<!-- 5 -->\n",
       "<g id=\"node6\" class=\"node\"><title>5</title>\n",
       "<path fill=\"#399de5\" fill-opacity=\"0.666667\" stroke=\"black\" d=\"M437.5,-306C437.5,-306 231.5,-306 231.5,-306 225.5,-306 219.5,-300 219.5,-294 219.5,-294 219.5,-235 219.5,-235 219.5,-229 225.5,-223 231.5,-223 231.5,-223 437.5,-223 437.5,-223 443.5,-223 449.5,-229 449.5,-235 449.5,-235 449.5,-294 449.5,-294 449.5,-300 443.5,-306 437.5,-306\"/>\n",
       "<text text-anchor=\"start\" x=\"227.5\" y=\"-290.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">worst concave points ≤ 0.112</text>\n",
       "<text text-anchor=\"start\" x=\"290.5\" y=\"-275.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.375</text>\n",
       "<text text-anchor=\"start\" x=\"289\" y=\"-260.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 8</text>\n",
       "<text text-anchor=\"start\" x=\"285.5\" y=\"-245.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [2, 6]</text>\n",
       "<text text-anchor=\"start\" x=\"281.5\" y=\"-230.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">class = benign</text>\n",
       "</g>\n",
       "<!-- 3&#45;&gt;5 -->\n",
       "<g id=\"edge5\" class=\"edge\"><title>3&#45;&gt;5</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M334.5,-341.907C334.5,-333.649 334.5,-324.864 334.5,-316.302\"/>\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"338,-316.021 334.5,-306.021 331,-316.021 338,-316.021\"/>\n",
       "</g>\n",
       "<!-- 6 -->\n",
       "<g id=\"node7\" class=\"node\"><title>6</title>\n",
       "<path fill=\"#399de5\" fill-opacity=\"0.831373\" stroke=\"black\" d=\"M245,-187C245,-187 52,-187 52,-187 46,-187 40,-181 40,-175 40,-175 40,-116 40,-116 40,-110 46,-104 52,-104 52,-104 245,-104 245,-104 251,-104 257,-110 257,-116 257,-116 257,-175 257,-175 257,-181 251,-187 245,-187\"/>\n",
       "<text text-anchor=\"start\" x=\"48\" y=\"-171.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">worst compactness ≤ 0.082</text>\n",
       "<text text-anchor=\"start\" x=\"104.5\" y=\"-156.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.245</text>\n",
       "<text text-anchor=\"start\" x=\"103\" y=\"-141.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 7</text>\n",
       "<text text-anchor=\"start\" x=\"99.5\" y=\"-126.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [1, 6]</text>\n",
       "<text text-anchor=\"start\" x=\"95.5\" y=\"-111.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">class = benign</text>\n",
       "</g>\n",
       "<!-- 5&#45;&gt;6 -->\n",
       "<g id=\"edge6\" class=\"edge\"><title>5&#45;&gt;6</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M269.968,-222.907C254.387,-213.106 237.626,-202.563 221.68,-192.533\"/>\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"223.246,-189.383 212.918,-187.021 219.519,-195.308 223.246,-189.383\"/>\n",
       "</g>\n",
       "<!-- 9 -->\n",
       "<g id=\"node10\" class=\"node\"><title>9</title>\n",
       "<path fill=\"#e58139\" stroke=\"black\" d=\"M408,-179.5C408,-179.5 287,-179.5 287,-179.5 281,-179.5 275,-173.5 275,-167.5 275,-167.5 275,-123.5 275,-123.5 275,-117.5 281,-111.5 287,-111.5 287,-111.5 408,-111.5 408,-111.5 414,-111.5 420,-117.5 420,-123.5 420,-123.5 420,-167.5 420,-167.5 420,-173.5 414,-179.5 408,-179.5\"/>\n",
       "<text text-anchor=\"start\" x=\"312.5\" y=\"-164.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.0</text>\n",
       "<text text-anchor=\"start\" x=\"302\" y=\"-149.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 1</text>\n",
       "<text text-anchor=\"start\" x=\"298.5\" y=\"-134.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [1, 0]</text>\n",
       "<text text-anchor=\"start\" x=\"283\" y=\"-119.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">class = malignant</text>\n",
       "</g>\n",
       "<!-- 5&#45;&gt;9 -->\n",
       "<g id=\"edge9\" class=\"edge\"><title>5&#45;&gt;9</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M339.01,-222.907C340.2,-212.204 341.487,-200.615 342.692,-189.776\"/>\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"346.189,-189.992 343.815,-179.667 339.232,-189.219 346.189,-189.992\"/>\n",
       "</g>\n",
       "<!-- 7 -->\n",
       "<g id=\"node8\" class=\"node\"><title>7</title>\n",
       "<path fill=\"#e58139\" stroke=\"black\" d=\"M133,-68C133,-68 12,-68 12,-68 6,-68 0,-62 0,-56 0,-56 0,-12 0,-12 0,-6 6,-0 12,-0 12,-0 133,-0 133,-0 139,-0 145,-6 145,-12 145,-12 145,-56 145,-56 145,-62 139,-68 133,-68\"/>\n",
       "<text text-anchor=\"start\" x=\"37.5\" y=\"-52.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.0</text>\n",
       "<text text-anchor=\"start\" x=\"27\" y=\"-37.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 1</text>\n",
       "<text text-anchor=\"start\" x=\"23.5\" y=\"-22.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [1, 0]</text>\n",
       "<text text-anchor=\"start\" x=\"8\" y=\"-7.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">class = malignant</text>\n",
       "</g>\n",
       "<!-- 6&#45;&gt;7 -->\n",
       "<g id=\"edge7\" class=\"edge\"><title>6&#45;&gt;7</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M120.2,-103.726C114.06,-94.879 107.557,-85.51 101.394,-76.6303\"/>\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"104.189,-74.5191 95.6121,-68.2996 98.4386,-78.5104 104.189,-74.5191\"/>\n",
       "</g>\n",
       "<!-- 8 -->\n",
       "<g id=\"node9\" class=\"node\"><title>8</title>\n",
       "<path fill=\"#399de5\" stroke=\"black\" d=\"M273.5,-68C273.5,-68 175.5,-68 175.5,-68 169.5,-68 163.5,-62 163.5,-56 163.5,-56 163.5,-12 163.5,-12 163.5,-6 169.5,-0 175.5,-0 175.5,-0 273.5,-0 273.5,-0 279.5,-0 285.5,-6 285.5,-12 285.5,-12 285.5,-56 285.5,-56 285.5,-62 279.5,-68 273.5,-68\"/>\n",
       "<text text-anchor=\"start\" x=\"189.5\" y=\"-52.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.0</text>\n",
       "<text text-anchor=\"start\" x=\"179\" y=\"-37.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 6</text>\n",
       "<text text-anchor=\"start\" x=\"175.5\" y=\"-22.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [0, 6]</text>\n",
       "<text text-anchor=\"start\" x=\"171.5\" y=\"-7.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">class = benign</text>\n",
       "</g>\n",
       "<!-- 6&#45;&gt;8 -->\n",
       "<g id=\"edge8\" class=\"edge\"><title>6&#45;&gt;8</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M176.8,-103.726C182.94,-94.879 189.443,-85.51 195.606,-76.6303\"/>\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"198.561,-78.5104 201.388,-68.2996 192.811,-74.5191 198.561,-78.5104\"/>\n",
       "</g>\n",
       "<!-- 11 -->\n",
       "<g id=\"node12\" class=\"node\"><title>11</title>\n",
       "<path fill=\"#e58139\" fill-opacity=\"0.749020\" stroke=\"black\" d=\"M615,-306C615,-306 480,-306 480,-306 474,-306 468,-300 468,-294 468,-294 468,-235 468,-235 468,-229 474,-223 480,-223 480,-223 615,-223 615,-223 621,-223 627,-229 627,-235 627,-235 627,-294 627,-294 627,-300 621,-306 615,-306\"/>\n",
       "<text text-anchor=\"start\" x=\"476\" y=\"-290.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">mean radius ≤ 14.8</text>\n",
       "<text text-anchor=\"start\" x=\"508\" y=\"-275.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.32</text>\n",
       "<text text-anchor=\"start\" x=\"502\" y=\"-260.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 5</text>\n",
       "<text text-anchor=\"start\" x=\"498.5\" y=\"-245.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [4, 1]</text>\n",
       "<text text-anchor=\"start\" x=\"483\" y=\"-230.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">class = malignant</text>\n",
       "</g>\n",
       "<!-- 10&#45;&gt;11 -->\n",
       "<g id=\"edge11\" class=\"edge\"><title>10&#45;&gt;11</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M599.744,-341.907C593.663,-333.014 587.164,-323.509 580.889,-314.331\"/>\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"583.74,-312.3 575.207,-306.021 577.962,-316.251 583.74,-312.3\"/>\n",
       "</g>\n",
       "<!-- 14 -->\n",
       "<g id=\"node15\" class=\"node\"><title>14</title>\n",
       "<path fill=\"#399de5\" stroke=\"black\" d=\"M755.5,-298.5C755.5,-298.5 657.5,-298.5 657.5,-298.5 651.5,-298.5 645.5,-292.5 645.5,-286.5 645.5,-286.5 645.5,-242.5 645.5,-242.5 645.5,-236.5 651.5,-230.5 657.5,-230.5 657.5,-230.5 755.5,-230.5 755.5,-230.5 761.5,-230.5 767.5,-236.5 767.5,-242.5 767.5,-242.5 767.5,-286.5 767.5,-286.5 767.5,-292.5 761.5,-298.5 755.5,-298.5\"/>\n",
       "<text text-anchor=\"start\" x=\"671.5\" y=\"-283.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.0</text>\n",
       "<text text-anchor=\"start\" x=\"661\" y=\"-268.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 4</text>\n",
       "<text text-anchor=\"start\" x=\"657.5\" y=\"-253.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [0, 4]</text>\n",
       "<text text-anchor=\"start\" x=\"653.5\" y=\"-238.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">class = benign</text>\n",
       "</g>\n",
       "<!-- 10&#45;&gt;14 -->\n",
       "<g id=\"edge14\" class=\"edge\"><title>10&#45;&gt;14</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M654.909,-341.907C662.508,-330.652 670.769,-318.418 678.407,-307.106\"/>\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"681.41,-308.913 684.105,-298.667 675.609,-304.996 681.41,-308.913\"/>\n",
       "</g>\n",
       "<!-- 12 -->\n",
       "<g id=\"node13\" class=\"node\"><title>12</title>\n",
       "<path fill=\"#e58139\" stroke=\"black\" d=\"M596,-179.5C596,-179.5 475,-179.5 475,-179.5 469,-179.5 463,-173.5 463,-167.5 463,-167.5 463,-123.5 463,-123.5 463,-117.5 469,-111.5 475,-111.5 475,-111.5 596,-111.5 596,-111.5 602,-111.5 608,-117.5 608,-123.5 608,-123.5 608,-167.5 608,-167.5 608,-173.5 602,-179.5 596,-179.5\"/>\n",
       "<text text-anchor=\"start\" x=\"500.5\" y=\"-164.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.0</text>\n",
       "<text text-anchor=\"start\" x=\"490\" y=\"-149.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 4</text>\n",
       "<text text-anchor=\"start\" x=\"486.5\" y=\"-134.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [4, 0]</text>\n",
       "<text text-anchor=\"start\" x=\"471\" y=\"-119.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">class = malignant</text>\n",
       "</g>\n",
       "<!-- 11&#45;&gt;12 -->\n",
       "<g id=\"edge12\" class=\"edge\"><title>11&#45;&gt;12</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M543.337,-222.907C542.239,-212.204 541.05,-200.615 539.939,-189.776\"/>\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"543.404,-189.258 538.902,-179.667 536.44,-189.972 543.404,-189.258\"/>\n",
       "</g>\n",
       "<!-- 13 -->\n",
       "<g id=\"node14\" class=\"node\"><title>13</title>\n",
       "<path fill=\"#399de5\" stroke=\"black\" d=\"M736.5,-179.5C736.5,-179.5 638.5,-179.5 638.5,-179.5 632.5,-179.5 626.5,-173.5 626.5,-167.5 626.5,-167.5 626.5,-123.5 626.5,-123.5 626.5,-117.5 632.5,-111.5 638.5,-111.5 638.5,-111.5 736.5,-111.5 736.5,-111.5 742.5,-111.5 748.5,-117.5 748.5,-123.5 748.5,-123.5 748.5,-167.5 748.5,-167.5 748.5,-173.5 742.5,-179.5 736.5,-179.5\"/>\n",
       "<text text-anchor=\"start\" x=\"652.5\" y=\"-164.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.0</text>\n",
       "<text text-anchor=\"start\" x=\"642\" y=\"-149.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 1</text>\n",
       "<text text-anchor=\"start\" x=\"638.5\" y=\"-134.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [0, 1]</text>\n",
       "<text text-anchor=\"start\" x=\"634.5\" y=\"-119.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">class = benign</text>\n",
       "</g>\n",
       "<!-- 11&#45;&gt;13 -->\n",
       "<g id=\"edge13\" class=\"edge\"><title>11&#45;&gt;13</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M596.072,-222.907C610.332,-210.99 625.905,-197.976 640.092,-186.12\"/>\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"642.384,-188.765 647.813,-179.667 637.895,-183.394 642.384,-188.765\"/>\n",
       "</g>\n",
       "<!-- 16 -->\n",
       "<g id=\"node17\" class=\"node\"><title>16</title>\n",
       "<path fill=\"#399de5\" fill-opacity=\"0.666667\" stroke=\"black\" d=\"M1046.5,-425C1046.5,-425 880.5,-425 880.5,-425 874.5,-425 868.5,-419 868.5,-413 868.5,-413 868.5,-354 868.5,-354 868.5,-348 874.5,-342 880.5,-342 880.5,-342 1046.5,-342 1046.5,-342 1052.5,-342 1058.5,-348 1058.5,-354 1058.5,-354 1058.5,-413 1058.5,-413 1058.5,-419 1052.5,-425 1046.5,-425\"/>\n",
       "<text text-anchor=\"start\" x=\"876.5\" y=\"-409.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">perimeter error ≤ 3.465</text>\n",
       "<text text-anchor=\"start\" x=\"919.5\" y=\"-394.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.375</text>\n",
       "<text text-anchor=\"start\" x=\"918\" y=\"-379.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 4</text>\n",
       "<text text-anchor=\"start\" x=\"914.5\" y=\"-364.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [1, 3]</text>\n",
       "<text text-anchor=\"start\" x=\"910.5\" y=\"-349.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">class = benign</text>\n",
       "</g>\n",
       "<!-- 15&#45;&gt;16 -->\n",
       "<g id=\"edge16\" class=\"edge\"><title>15&#45;&gt;16</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M1035.99,-460.907C1027.29,-451.742 1017.98,-441.927 1009.03,-432.489\"/>\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"1011.36,-429.867 1001.94,-425.021 1006.29,-434.684 1011.36,-429.867\"/>\n",
       "</g>\n",
       "<!-- 19 -->\n",
       "<g id=\"node20\" class=\"node\"><title>19</title>\n",
       "<path fill=\"#e58139\" fill-opacity=\"0.858824\" stroke=\"black\" d=\"M1282,-425C1282,-425 1089,-425 1089,-425 1083,-425 1077,-419 1077,-413 1077,-413 1077,-354 1077,-354 1077,-348 1083,-342 1089,-342 1089,-342 1282,-342 1282,-342 1288,-342 1294,-348 1294,-354 1294,-354 1294,-413 1294,-413 1294,-419 1288,-425 1282,-425\"/>\n",
       "<text text-anchor=\"start\" x=\"1085\" y=\"-409.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">worst compactness ≤ 0.142</text>\n",
       "<text text-anchor=\"start\" x=\"1141.5\" y=\"-394.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.219</text>\n",
       "<text text-anchor=\"start\" x=\"1135.5\" y=\"-379.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 16</text>\n",
       "<text text-anchor=\"start\" x=\"1132\" y=\"-364.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [14, 2]</text>\n",
       "<text text-anchor=\"start\" x=\"1121\" y=\"-349.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">class = malignant</text>\n",
       "</g>\n",
       "<!-- 15&#45;&gt;19 -->\n",
       "<g id=\"edge19\" class=\"edge\"><title>15&#45;&gt;19</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M1113.01,-460.907C1121.71,-451.742 1131.02,-441.927 1139.97,-432.489\"/>\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"1142.71,-434.684 1147.06,-425.021 1137.64,-429.867 1142.71,-434.684\"/>\n",
       "</g>\n",
       "<!-- 17 -->\n",
       "<g id=\"node18\" class=\"node\"><title>17</title>\n",
       "<path fill=\"#399de5\" stroke=\"black\" d=\"M895.5,-298.5C895.5,-298.5 797.5,-298.5 797.5,-298.5 791.5,-298.5 785.5,-292.5 785.5,-286.5 785.5,-286.5 785.5,-242.5 785.5,-242.5 785.5,-236.5 791.5,-230.5 797.5,-230.5 797.5,-230.5 895.5,-230.5 895.5,-230.5 901.5,-230.5 907.5,-236.5 907.5,-242.5 907.5,-242.5 907.5,-286.5 907.5,-286.5 907.5,-292.5 901.5,-298.5 895.5,-298.5\"/>\n",
       "<text text-anchor=\"start\" x=\"811.5\" y=\"-283.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.0</text>\n",
       "<text text-anchor=\"start\" x=\"801\" y=\"-268.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 3</text>\n",
       "<text text-anchor=\"start\" x=\"797.5\" y=\"-253.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [0, 3]</text>\n",
       "<text text-anchor=\"start\" x=\"793.5\" y=\"-238.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">class = benign</text>\n",
       "</g>\n",
       "<!-- 16&#45;&gt;17 -->\n",
       "<g id=\"edge17\" class=\"edge\"><title>16&#45;&gt;17</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M922.907,-341.907C911.211,-330.211 898.457,-317.457 886.78,-305.78\"/>\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"889.213,-303.263 879.667,-298.667 884.263,-308.213 889.213,-303.263\"/>\n",
       "</g>\n",
       "<!-- 18 -->\n",
       "<g id=\"node19\" class=\"node\"><title>18</title>\n",
       "<path fill=\"#e58139\" stroke=\"black\" d=\"M1059,-298.5C1059,-298.5 938,-298.5 938,-298.5 932,-298.5 926,-292.5 926,-286.5 926,-286.5 926,-242.5 926,-242.5 926,-236.5 932,-230.5 938,-230.5 938,-230.5 1059,-230.5 1059,-230.5 1065,-230.5 1071,-236.5 1071,-242.5 1071,-242.5 1071,-286.5 1071,-286.5 1071,-292.5 1065,-298.5 1059,-298.5\"/>\n",
       "<text text-anchor=\"start\" x=\"963.5\" y=\"-283.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.0</text>\n",
       "<text text-anchor=\"start\" x=\"953\" y=\"-268.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 1</text>\n",
       "<text text-anchor=\"start\" x=\"949.5\" y=\"-253.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [1, 0]</text>\n",
       "<text text-anchor=\"start\" x=\"934\" y=\"-238.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">class = malignant</text>\n",
       "</g>\n",
       "<!-- 16&#45;&gt;18 -->\n",
       "<g id=\"edge18\" class=\"edge\"><title>16&#45;&gt;18</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M975.643,-341.907C978.878,-331.094 982.383,-319.376 985.654,-308.441\"/>\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"989.065,-309.251 988.578,-298.667 982.359,-307.244 989.065,-309.251\"/>\n",
       "</g>\n",
       "<!-- 20 -->\n",
       "<g id=\"node21\" class=\"node\"><title>20</title>\n",
       "<path fill=\"#399de5\" stroke=\"black\" d=\"M1199.5,-298.5C1199.5,-298.5 1101.5,-298.5 1101.5,-298.5 1095.5,-298.5 1089.5,-292.5 1089.5,-286.5 1089.5,-286.5 1089.5,-242.5 1089.5,-242.5 1089.5,-236.5 1095.5,-230.5 1101.5,-230.5 1101.5,-230.5 1199.5,-230.5 1199.5,-230.5 1205.5,-230.5 1211.5,-236.5 1211.5,-242.5 1211.5,-242.5 1211.5,-286.5 1211.5,-286.5 1211.5,-292.5 1205.5,-298.5 1199.5,-298.5\"/>\n",
       "<text text-anchor=\"start\" x=\"1115.5\" y=\"-283.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.0</text>\n",
       "<text text-anchor=\"start\" x=\"1105\" y=\"-268.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 1</text>\n",
       "<text text-anchor=\"start\" x=\"1101.5\" y=\"-253.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [0, 1]</text>\n",
       "<text text-anchor=\"start\" x=\"1097.5\" y=\"-238.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">class = benign</text>\n",
       "</g>\n",
       "<!-- 19&#45;&gt;20 -->\n",
       "<g id=\"edge20\" class=\"edge\"><title>19&#45;&gt;20</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M1173.36,-341.907C1170.12,-331.094 1166.62,-319.376 1163.35,-308.441\"/>\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"1166.64,-307.244 1160.42,-298.667 1159.93,-309.251 1166.64,-307.244\"/>\n",
       "</g>\n",
       "<!-- 21 -->\n",
       "<g id=\"node22\" class=\"node\"><title>21</title>\n",
       "<path fill=\"#e58139\" fill-opacity=\"0.929412\" stroke=\"black\" d=\"M1413,-306C1413,-306 1242,-306 1242,-306 1236,-306 1230,-300 1230,-294 1230,-294 1230,-235 1230,-235 1230,-229 1236,-223 1242,-223 1242,-223 1413,-223 1413,-223 1419,-223 1425,-229 1425,-235 1425,-235 1425,-294 1425,-294 1425,-300 1419,-306 1413,-306\"/>\n",
       "<text text-anchor=\"start\" x=\"1238\" y=\"-290.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">worst symmetry ≤ 0.278</text>\n",
       "<text text-anchor=\"start\" x=\"1283.5\" y=\"-275.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.124</text>\n",
       "<text text-anchor=\"start\" x=\"1277.5\" y=\"-260.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 15</text>\n",
       "<text text-anchor=\"start\" x=\"1274\" y=\"-245.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [14, 1]</text>\n",
       "<text text-anchor=\"start\" x=\"1263\" y=\"-230.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">class = malignant</text>\n",
       "</g>\n",
       "<!-- 19&#45;&gt;21 -->\n",
       "<g id=\"edge21\" class=\"edge\"><title>19&#45;&gt;21</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M1234.77,-341.907C1246.22,-332.469 1258.51,-322.343 1270.28,-312.649\"/>\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"1272.83,-315.081 1278.32,-306.021 1268.38,-309.679 1272.83,-315.081\"/>\n",
       "</g>\n",
       "<!-- 22 -->\n",
       "<g id=\"node23\" class=\"node\"><title>22</title>\n",
       "<path fill=\"#e58139\" fill-opacity=\"0.666667\" stroke=\"black\" d=\"M1316,-187C1316,-187 1157,-187 1157,-187 1151,-187 1145,-181 1145,-175 1145,-175 1145,-116 1145,-116 1145,-110 1151,-104 1157,-104 1157,-104 1316,-104 1316,-104 1322,-104 1328,-110 1328,-116 1328,-116 1328,-175 1328,-175 1328,-181 1322,-187 1316,-187\"/>\n",
       "<text text-anchor=\"start\" x=\"1153\" y=\"-171.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">worst texture ≤ 25.525</text>\n",
       "<text text-anchor=\"start\" x=\"1192.5\" y=\"-156.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.375</text>\n",
       "<text text-anchor=\"start\" x=\"1191\" y=\"-141.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 4</text>\n",
       "<text text-anchor=\"start\" x=\"1187.5\" y=\"-126.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [3, 1]</text>\n",
       "<text text-anchor=\"start\" x=\"1172\" y=\"-111.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">class = malignant</text>\n",
       "</g>\n",
       "<!-- 21&#45;&gt;22 -->\n",
       "<g id=\"edge22\" class=\"edge\"><title>21&#45;&gt;22</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M1295.93,-222.907C1288.94,-213.923 1281.47,-204.315 1274.26,-195.05\"/>\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"1276.92,-192.766 1268.02,-187.021 1271.39,-197.063 1276.92,-192.766\"/>\n",
       "</g>\n",
       "<!-- 25 -->\n",
       "<g id=\"node26\" class=\"node\"><title>25</title>\n",
       "<path fill=\"#e58139\" stroke=\"black\" d=\"M1479,-179.5C1479,-179.5 1358,-179.5 1358,-179.5 1352,-179.5 1346,-173.5 1346,-167.5 1346,-167.5 1346,-123.5 1346,-123.5 1346,-117.5 1352,-111.5 1358,-111.5 1358,-111.5 1479,-111.5 1479,-111.5 1485,-111.5 1491,-117.5 1491,-123.5 1491,-123.5 1491,-167.5 1491,-167.5 1491,-173.5 1485,-179.5 1479,-179.5\"/>\n",
       "<text text-anchor=\"start\" x=\"1383.5\" y=\"-164.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.0</text>\n",
       "<text text-anchor=\"start\" x=\"1368.5\" y=\"-149.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 11</text>\n",
       "<text text-anchor=\"start\" x=\"1365\" y=\"-134.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [11, 0]</text>\n",
       "<text text-anchor=\"start\" x=\"1354\" y=\"-119.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">class = malignant</text>\n",
       "</g>\n",
       "<!-- 21&#45;&gt;25 -->\n",
       "<g id=\"edge25\" class=\"edge\"><title>21&#45;&gt;25</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M1359.07,-222.907C1367.91,-211.542 1377.53,-199.178 1386.4,-187.774\"/>\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"1389.33,-189.709 1392.7,-179.667 1383.8,-185.412 1389.33,-189.709\"/>\n",
       "</g>\n",
       "<!-- 23 -->\n",
       "<g id=\"node24\" class=\"node\"><title>23</title>\n",
       "<path fill=\"#399de5\" stroke=\"black\" d=\"M1209.5,-68C1209.5,-68 1111.5,-68 1111.5,-68 1105.5,-68 1099.5,-62 1099.5,-56 1099.5,-56 1099.5,-12 1099.5,-12 1099.5,-6 1105.5,-0 1111.5,-0 1111.5,-0 1209.5,-0 1209.5,-0 1215.5,-0 1221.5,-6 1221.5,-12 1221.5,-12 1221.5,-56 1221.5,-56 1221.5,-62 1215.5,-68 1209.5,-68\"/>\n",
       "<text text-anchor=\"start\" x=\"1125.5\" y=\"-52.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.0</text>\n",
       "<text text-anchor=\"start\" x=\"1115\" y=\"-37.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 1</text>\n",
       "<text text-anchor=\"start\" x=\"1111.5\" y=\"-22.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [0, 1]</text>\n",
       "<text text-anchor=\"start\" x=\"1107.5\" y=\"-7.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">class = benign</text>\n",
       "</g>\n",
       "<!-- 22&#45;&gt;23 -->\n",
       "<g id=\"edge23\" class=\"edge\"><title>22&#45;&gt;23</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M1208.2,-103.726C1202.06,-94.879 1195.56,-85.51 1189.39,-76.6303\"/>\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"1192.19,-74.5191 1183.61,-68.2996 1186.44,-78.5104 1192.19,-74.5191\"/>\n",
       "</g>\n",
       "<!-- 24 -->\n",
       "<g id=\"node25\" class=\"node\"><title>24</title>\n",
       "<path fill=\"#e58139\" stroke=\"black\" d=\"M1373,-68C1373,-68 1252,-68 1252,-68 1246,-68 1240,-62 1240,-56 1240,-56 1240,-12 1240,-12 1240,-6 1246,-0 1252,-0 1252,-0 1373,-0 1373,-0 1379,-0 1385,-6 1385,-12 1385,-12 1385,-56 1385,-56 1385,-62 1379,-68 1373,-68\"/>\n",
       "<text text-anchor=\"start\" x=\"1277.5\" y=\"-52.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.0</text>\n",
       "<text text-anchor=\"start\" x=\"1267\" y=\"-37.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 3</text>\n",
       "<text text-anchor=\"start\" x=\"1263.5\" y=\"-22.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [3, 0]</text>\n",
       "<text text-anchor=\"start\" x=\"1248\" y=\"-7.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">class = malignant</text>\n",
       "</g>\n",
       "<!-- 22&#45;&gt;24 -->\n",
       "<g id=\"edge24\" class=\"edge\"><title>22&#45;&gt;24</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M1264.8,-103.726C1270.94,-94.879 1277.44,-85.51 1283.61,-76.6303\"/>\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"1286.56,-78.5104 1289.39,-68.2996 1280.81,-74.5191 1286.56,-78.5104\"/>\n",
       "</g>\n",
       "<!-- 27 -->\n",
       "<g id=\"node28\" class=\"node\"><title>27</title>\n",
       "<path fill=\"#e58139\" fill-opacity=\"0.960784\" stroke=\"black\" d=\"M1660,-544C1660,-544 1481,-544 1481,-544 1475,-544 1469,-538 1469,-532 1469,-532 1469,-473 1469,-473 1469,-467 1475,-461 1481,-461 1481,-461 1660,-461 1660,-461 1666,-461 1672,-467 1672,-473 1672,-473 1672,-532 1672,-532 1672,-538 1666,-544 1660,-544\"/>\n",
       "<text text-anchor=\"start\" x=\"1477\" y=\"-528.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">worst perimeter ≤ 103.15</text>\n",
       "<text text-anchor=\"start\" x=\"1526.5\" y=\"-513.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.073</text>\n",
       "<text text-anchor=\"start\" x=\"1516\" y=\"-498.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 131</text>\n",
       "<text text-anchor=\"start\" x=\"1512.5\" y=\"-483.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [126, 5]</text>\n",
       "<text text-anchor=\"start\" x=\"1506\" y=\"-468.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">class = malignant</text>\n",
       "</g>\n",
       "<!-- 26&#45;&gt;27 -->\n",
       "<g id=\"edge27\" class=\"edge\"><title>26&#45;&gt;27</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M1570.5,-579.907C1570.5,-571.649 1570.5,-562.864 1570.5,-554.302\"/>\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"1574,-554.021 1570.5,-544.021 1567,-554.021 1574,-554.021\"/>\n",
       "</g>\n",
       "<!-- 36 -->\n",
       "<g id=\"node37\" class=\"node\"><title>36</title>\n",
       "<path fill=\"#399de5\" stroke=\"black\" d=\"M1800.5,-536.5C1800.5,-536.5 1702.5,-536.5 1702.5,-536.5 1696.5,-536.5 1690.5,-530.5 1690.5,-524.5 1690.5,-524.5 1690.5,-480.5 1690.5,-480.5 1690.5,-474.5 1696.5,-468.5 1702.5,-468.5 1702.5,-468.5 1800.5,-468.5 1800.5,-468.5 1806.5,-468.5 1812.5,-474.5 1812.5,-480.5 1812.5,-480.5 1812.5,-524.5 1812.5,-524.5 1812.5,-530.5 1806.5,-536.5 1800.5,-536.5\"/>\n",
       "<text text-anchor=\"start\" x=\"1716.5\" y=\"-521.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.0</text>\n",
       "<text text-anchor=\"start\" x=\"1706\" y=\"-506.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 2</text>\n",
       "<text text-anchor=\"start\" x=\"1702.5\" y=\"-491.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [0, 2]</text>\n",
       "<text text-anchor=\"start\" x=\"1698.5\" y=\"-476.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">class = benign</text>\n",
       "</g>\n",
       "<!-- 26&#45;&gt;36 -->\n",
       "<g id=\"edge36\" class=\"edge\"><title>26&#45;&gt;36</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M1633.3,-579.907C1652.25,-567.659 1672.99,-554.252 1691.73,-542.135\"/>\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"1693.69,-545.035 1700.19,-536.667 1689.89,-539.156 1693.69,-545.035\"/>\n",
       "</g>\n",
       "<!-- 28 -->\n",
       "<g id=\"node29\" class=\"node\"><title>28</title>\n",
       "<path fill=\"#399de5\" fill-opacity=\"0.498039\" stroke=\"black\" d=\"M1658.5,-425C1658.5,-425 1482.5,-425 1482.5,-425 1476.5,-425 1470.5,-419 1470.5,-413 1470.5,-413 1470.5,-354 1470.5,-354 1470.5,-348 1476.5,-342 1482.5,-342 1482.5,-342 1658.5,-342 1658.5,-342 1664.5,-342 1670.5,-348 1670.5,-354 1670.5,-354 1670.5,-413 1670.5,-413 1670.5,-419 1664.5,-425 1658.5,-425\"/>\n",
       "<text text-anchor=\"start\" x=\"1478.5\" y=\"-409.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">worst smoothness ≤ 0.17</text>\n",
       "<text text-anchor=\"start\" x=\"1526.5\" y=\"-394.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.444</text>\n",
       "<text text-anchor=\"start\" x=\"1525\" y=\"-379.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 6</text>\n",
       "<text text-anchor=\"start\" x=\"1521.5\" y=\"-364.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [2, 4]</text>\n",
       "<text text-anchor=\"start\" x=\"1517.5\" y=\"-349.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">class = benign</text>\n",
       "</g>\n",
       "<!-- 27&#45;&gt;28 -->\n",
       "<g id=\"edge28\" class=\"edge\"><title>27&#45;&gt;28</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M1570.5,-460.907C1570.5,-452.649 1570.5,-443.864 1570.5,-435.302\"/>\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"1574,-435.021 1570.5,-425.021 1567,-435.021 1574,-435.021\"/>\n",
       "</g>\n",
       "<!-- 31 -->\n",
       "<g id=\"node32\" class=\"node\"><title>31</title>\n",
       "<path fill=\"#e58139\" fill-opacity=\"0.992157\" stroke=\"black\" d=\"M1929,-425C1929,-425 1768,-425 1768,-425 1762,-425 1756,-419 1756,-413 1756,-413 1756,-354 1756,-354 1756,-348 1762,-342 1768,-342 1768,-342 1929,-342 1929,-342 1935,-342 1941,-348 1941,-354 1941,-354 1941,-413 1941,-413 1941,-419 1935,-425 1929,-425\"/>\n",
       "<text text-anchor=\"start\" x=\"1764\" y=\"-409.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">mean texture ≤ 15.345</text>\n",
       "<text text-anchor=\"start\" x=\"1804.5\" y=\"-394.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.016</text>\n",
       "<text text-anchor=\"start\" x=\"1794\" y=\"-379.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 125</text>\n",
       "<text text-anchor=\"start\" x=\"1790.5\" y=\"-364.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [124, 1]</text>\n",
       "<text text-anchor=\"start\" x=\"1784\" y=\"-349.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">class = malignant</text>\n",
       "</g>\n",
       "<!-- 27&#45;&gt;31 -->\n",
       "<g id=\"edge31\" class=\"edge\"><title>27&#45;&gt;31</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M1666.95,-460.907C1692.65,-450.092 1720.5,-438.372 1746.48,-427.435\"/>\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"1748.11,-430.549 1755.97,-423.444 1745.39,-424.097 1748.11,-430.549\"/>\n",
       "</g>\n",
       "<!-- 29 -->\n",
       "<g id=\"node30\" class=\"node\"><title>29</title>\n",
       "<path fill=\"#399de5\" stroke=\"black\" d=\"M1553.5,-298.5C1553.5,-298.5 1455.5,-298.5 1455.5,-298.5 1449.5,-298.5 1443.5,-292.5 1443.5,-286.5 1443.5,-286.5 1443.5,-242.5 1443.5,-242.5 1443.5,-236.5 1449.5,-230.5 1455.5,-230.5 1455.5,-230.5 1553.5,-230.5 1553.5,-230.5 1559.5,-230.5 1565.5,-236.5 1565.5,-242.5 1565.5,-242.5 1565.5,-286.5 1565.5,-286.5 1565.5,-292.5 1559.5,-298.5 1553.5,-298.5\"/>\n",
       "<text text-anchor=\"start\" x=\"1469.5\" y=\"-283.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.0</text>\n",
       "<text text-anchor=\"start\" x=\"1459\" y=\"-268.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 4</text>\n",
       "<text text-anchor=\"start\" x=\"1455.5\" y=\"-253.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [0, 4]</text>\n",
       "<text text-anchor=\"start\" x=\"1451.5\" y=\"-238.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">class = benign</text>\n",
       "</g>\n",
       "<!-- 28&#45;&gt;29 -->\n",
       "<g id=\"edge29\" class=\"edge\"><title>28&#45;&gt;29</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M1547.6,-341.907C1541.31,-330.763 1534.49,-318.658 1528.16,-307.439\"/>\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"1531.17,-305.657 1523.21,-298.667 1525.07,-309.096 1531.17,-305.657\"/>\n",
       "</g>\n",
       "<!-- 30 -->\n",
       "<g id=\"node31\" class=\"node\"><title>30</title>\n",
       "<path fill=\"#e58139\" stroke=\"black\" d=\"M1717,-298.5C1717,-298.5 1596,-298.5 1596,-298.5 1590,-298.5 1584,-292.5 1584,-286.5 1584,-286.5 1584,-242.5 1584,-242.5 1584,-236.5 1590,-230.5 1596,-230.5 1596,-230.5 1717,-230.5 1717,-230.5 1723,-230.5 1729,-236.5 1729,-242.5 1729,-242.5 1729,-286.5 1729,-286.5 1729,-292.5 1723,-298.5 1717,-298.5\"/>\n",
       "<text text-anchor=\"start\" x=\"1621.5\" y=\"-283.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.0</text>\n",
       "<text text-anchor=\"start\" x=\"1611\" y=\"-268.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 2</text>\n",
       "<text text-anchor=\"start\" x=\"1607.5\" y=\"-253.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [2, 0]</text>\n",
       "<text text-anchor=\"start\" x=\"1592\" y=\"-238.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">class = malignant</text>\n",
       "</g>\n",
       "<!-- 28&#45;&gt;30 -->\n",
       "<g id=\"edge30\" class=\"edge\"><title>28&#45;&gt;30</title>\n",
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      ],
      "text/plain": [
       "<graphviz.files.Source at 0x7f908e3ef0b8>"
      ]
     },
     "execution_count": 28,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### Your solution\n",
    "#raise NotImplementedError\n",
    "\n",
    "dot_data = tree.export_graphviz(clf_grid.best_estimator_, out_file=None, \n",
    "                         feature_names=breast.feature_names,  \n",
    "                         class_names=breast.target_names,  \n",
    "                         filled=True, rounded=True,  \n",
    "                         special_characters=True)  \n",
    "graph = graphviz.Source(dot_data)  \n",
    "graph"
   ]
  },
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   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "ScvHV4yefNrz",
    "lang": "pl"
   },
   "source": [
    "**Krok 6:** Czas na zabawę! Spróbuj dopasować inne parametry i poprawić powyższy wynik."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "lang": "en"
   },
   "source": [
    "**Step 6:** Time to have fun! Try adjusting other parameters and improving the above result."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "RgYXrPV8diHh"
   },
   "outputs": [],
   "source": []
  }
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