SKLearn 13, Classification Task dengan Naive bayes.ipynb

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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 13 Classification Task dengan Naive Bayes\n",
    "\n",
    "Referensi: https://en.wikipedia.org/wiki/Naive_Bayes_classifier\n",
    "\n",
    "###  Bayes' Theorem\n",
    "Bayes' theorem menawarkan suatu formula untuk menghitung nilai probability dari suatu event dengan memanfaatkan pengetahuan sebelumnya dari kondisi terkait; atau sering kali dikenal dengan istilah conditional probability.\n",
    "\n",
    "![image.png](attachment:image.png)\n",
    "\n",
    "### Mengapa disebut Naive?\n",
    "- Karena sewaktu kita mendefinisikan Likelihood \n",
    "- kita mengasumsikan conditionally independent terhadap demikian sebaliknya.\n",
    "- Sehingga dapat diformulasikan sebagai berikut:\n",
    "\n",
    "## Dataset: Breast Cancer Wisconsin (Diagnostic)\n",
    "Referensi: https://archive.ics.uci.edu/ml/datasets/Breast+Cancer+Wisconsin+(Diagnostic)\n",
    "\n",
    "#### Load Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ".. _breast_cancer_dataset:\n",
      "\n",
      "Breast cancer wisconsin (diagnostic) dataset\n",
      "--------------------------------------------\n",
      "\n",
      "**Data Set Characteristics:**\n",
      "\n",
      "    :Number of Instances: 569\n",
      "\n",
      "    :Number of Attributes: 30 numeric, predictive attributes and the class\n",
      "\n",
      "    :Attribute Information:\n",
      "        - radius (mean of distances from center to points on the perimeter)\n",
      "        - texture (standard deviation of gray-scale values)\n",
      "        - perimeter\n",
      "        - area\n",
      "        - smoothness (local variation in radius lengths)\n",
      "        - compactness (perimeter^2 / area - 1.0)\n",
      "        - concavity (severity of concave portions of the contour)\n",
      "        - concave points (number of concave portions of the contour)\n",
      "        - symmetry\n",
      "        - fractal dimension (\"coastline approximation\" - 1)\n",
      "\n",
      "        The mean, standard error, and \"worst\" or largest (mean of the three\n",
      "        worst/largest values) of these features were computed for each image,\n",
      "        resulting in 30 features.  For instance, field 0 is Mean Radius, field\n",
      "        10 is Radius SE, field 20 is Worst Radius.\n",
      "\n",
      "        - class:\n",
      "                - WDBC-Malignant\n",
      "                - WDBC-Benign\n",
      "\n",
      "    :Summary Statistics:\n",
      "\n",
      "    ===================================== ====== ======\n",
      "                                           Min    Max\n",
      "    ===================================== ====== ======\n",
      "    radius (mean):                        6.981  28.11\n",
      "    texture (mean):                       9.71   39.28\n",
      "    perimeter (mean):                     43.79  188.5\n",
      "    area (mean):                          143.5  2501.0\n",
      "    smoothness (mean):                    0.053  0.163\n",
      "    compactness (mean):                   0.019  0.345\n",
      "    concavity (mean):                     0.0    0.427\n",
      "    concave points (mean):                0.0    0.201\n",
      "    symmetry (mean):                      0.106  0.304\n",
      "    fractal dimension (mean):             0.05   0.097\n",
      "    radius (standard error):              0.112  2.873\n",
      "    texture (standard error):             0.36   4.885\n",
      "    perimeter (standard error):           0.757  21.98\n",
      "    area (standard error):                6.802  542.2\n",
      "    smoothness (standard error):          0.002  0.031\n",
      "    compactness (standard error):         0.002  0.135\n",
      "    concavity (standard error):           0.0    0.396\n",
      "    concave points (standard error):      0.0    0.053\n",
      "    symmetry (standard error):            0.008  0.079\n",
      "    fractal dimension (standard error):   0.001  0.03\n",
      "    radius (worst):                       7.93   36.04\n",
      "    texture (worst):                      12.02  49.54\n",
      "    perimeter (worst):                    50.41  251.2\n",
      "    area (worst):                         185.2  4254.0\n",
      "    smoothness (worst):                   0.071  0.223\n",
      "    compactness (worst):                  0.027  1.058\n",
      "    concavity (worst):                    0.0    1.252\n",
      "    concave points (worst):               0.0    0.291\n",
      "    symmetry (worst):                     0.156  0.664\n",
      "    fractal dimension (worst):            0.055  0.208\n",
      "    ===================================== ====== ======\n",
      "\n",
      "    :Missing Attribute Values: None\n",
      "\n",
      "    :Class Distribution: 212 - Malignant, 357 - Benign\n",
      "\n",
      "    :Creator:  Dr. William H. Wolberg, W. Nick Street, Olvi L. Mangasarian\n",
      "\n",
      "    :Donor: Nick Street\n",
      "\n",
      "    :Date: November, 1995\n",
      "\n",
      "This is a copy of UCI ML Breast Cancer Wisconsin (Diagnostic) datasets.\n",
      "https://goo.gl/U2Uwz2\n",
      "\n",
      "Features are computed from a digitized image of a fine needle\n",
      "aspirate (FNA) of a breast mass.  They describe\n",
      "characteristics of the cell nuclei present in the image.\n",
      "\n",
      "Separating plane described above was obtained using\n",
      "Multisurface Method-Tree (MSM-T) [K. P. Bennett, \"Decision Tree\n",
      "Construction Via Linear Programming.\" Proceedings of the 4th\n",
      "Midwest Artificial Intelligence and Cognitive Science Society,\n",
      "pp. 97-101, 1992], a classification method which uses linear\n",
      "programming to construct a decision tree.  Relevant features\n",
      "were selected using an exhaustive search in the space of 1-4\n",
      "features and 1-3 separating planes.\n",
      "\n",
      "The actual linear program used to obtain the separating plane\n",
      "in the 3-dimensional space is that described in:\n",
      "[K. P. Bennett and O. L. Mangasarian: \"Robust Linear\n",
      "Programming Discrimination of Two Linearly Inseparable Sets\",\n",
      "Optimization Methods and Software 1, 1992, 23-34].\n",
      "\n",
      "This database is also available through the UW CS ftp server:\n",
      "\n",
      "ftp ftp.cs.wisc.edu\n",
      "cd math-prog/cpo-dataset/machine-learn/WDBC/\n",
      "\n",
      ".. topic:: References\n",
      "\n",
      "   - W.N. Street, W.H. Wolberg and O.L. Mangasarian. Nuclear feature extraction \n",
      "     for breast tumor diagnosis. IS&T/SPIE 1993 International Symposium on \n",
      "     Electronic Imaging: Science and Technology, volume 1905, pages 861-870,\n",
      "     San Jose, CA, 1993.\n",
      "   - O.L. Mangasarian, W.N. Street and W.H. Wolberg. Breast cancer diagnosis and \n",
      "     prognosis via linear programming. Operations Research, 43(4), pages 570-577, \n",
      "     July-August 1995.\n",
      "   - W.H. Wolberg, W.N. Street, and O.L. Mangasarian. Machine learning techniques\n",
      "     to diagnose breast cancer from fine-needle aspirates. Cancer Letters 77 (1994) \n",
      "     163-171.\n"
     ]
    }
   ],
   "source": [
    "from sklearn.datasets import load_breast_cancer\n",
    "\n",
    "print(load_breast_cancer().DESCR)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(569, 30)"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# load_breast_cancer?\n",
    "X, y = load_breast_cancer(return_X_y=True)\n",
    "X.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Training & Testing Set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X_train shape (455, 30)\n",
      "X_test shape (114, 30)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X,\n",
    "                                                    y,\n",
    "                                                    test_size=0.2,\n",
    "                                                    random_state=0)\n",
    "\n",
    "print(f'X_train shape {X_train.shape}')\n",
    "print(f'X_test shape {X_test.shape}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Naive Bayes dengan Scikit Learn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9298245614035088"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn.metrics import accuracy_score\n",
    "\n",
    "model = GaussianNB()\n",
    "model.fit(X_train, y_train)\n",
    "y_pred = model.predict(X_test)\n",
    "accuracy_score(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9298245614035088"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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