Mushroom Classifier Using C# and ML.Net

ML Task	Screnario	Platform	Langauge	ML.NET version	Algorithms	Data type	App type
Binary Classification	Classifiy Mushrooms(Edible/poisonous)	Dot Net Core 2.1	C#	V 1.3.1	AveragedPerceptron and OneVersusAll	csv file	Console app

Problem

This project demonstrates the application of ML.Net to classify the mushrooms whether they are edible or poisonous. This type of task is very popular in the machine learning world and is often referred to as a Two-class or Binary classification problem. The purpose of this project is to see how can we leverage the wonderful capabilities of ML.Net to implement machine learning based features into our .net applications.

Data

This dataset includes descriptions of hypothetical samples corresponding to 23 species of gilled mushrooms in the Agaricus and Lepiota Family Mushroom drawn from The Audubon Society Field Guide to North American Mushrooms (1981). Each species is identified as definitely edible, definitely poisonous, or of unknown edibility and not recommended. This latter class was combined with the poisonous one. The Guide clearly states that there is no simple rule for determining the edibility of a mushroom; no rule like "leaflets three, let it be'' for Poisonous Oak and Ivy.

Attribute Information

Features

cap-shape	cap-surface	cap-color	bruises	odor	gill-attachment	gill-spacing	gill-size	gill-color	stalk-shape	stalk-root	stalk-surface-above-ring	stalk-surface-below-ring	stalk-color-above-ring	stalk-color-below-ring	veil-type	veil-color	ring-number	ring-type	spore-print-color	population	habitat
bell=b,conical=c,convex=x,flat=f, knobbed=k,sunken=s	fibrous=f,grooves=g,scaly=y,smooth=s	brown=n,buff=b,cinnamon=c,gray=g,green=r,pink=p,purple=u,red=e,white=w,yellow=y	bruises=t,no=f	almond=a,anise=l,creosote=c,fishy=y,foul=f,musty=m,none=n,pungent=p,spicy=s	attached=a,descending=d,free=f,notched=n	close=c,crowded=w,distant=d	broad=b,narrow=n	black=k,brown=n,buff=b,chocolate=h,gray=g, green=r,orange=o,pink=p,purple=u,red=e,white=w,yellow=y	enlarging=e,tapering=t	bulbous=b,club=c,cup=u,equal=e,rhizomorphs=z,rooted=r,missing=?	fibrous=f,scaly=y,silky=k,smooth=s	fibrous=f,scaly=y,silky=k,smooth=s	brown=n,buff=b,cinnamon=c,gray=g,orange=o,pink=p,red=e,white=w,yellow=y	brown=n,buff=b,cinnamon=c,gray=g,orange=o,pink=p,red=e,white=w,yellow=y	partial=p,universal=u	brown=n,orange=o,white=w,yellow=y	none=n,one=o,two=t	cobwebby=c,evanescent=e,flaring=f,large=l,none=n,pendant=p,sheathing=s,zone=z	black=k,brown=n,buff=b,chocolate=h,green=r,orange=o,purple=u,white=w,yellow=y	abundant=a,clustered=c,numerous=n,scattered=s,several=v,solitary=y	grasses=g,leaves=l,meadows=m,paths=p,urban=u,waste=w,woods=d

Label (Class)

Label
edible=e, poisonous=p

Solution

To solve this problem, we perform the following steps

Step 1:

• Load the dataset from csv data file
• Divide it into training and testing sets in the ratio of 75:25

Step 2:

• Preprocess the data - Create an estimator and transform the data to the numeric vectors so that it can be used by ML algorithm.
• Choose and append the classification algorithm to the pipeline

Step 3:

• Crossvalidate the model to check its performance. Cross-validation provides the model's performance based on various evaluation metrics like Average MicroAccuracy, Average LogLoss, Average MacroAccuracy, etc.

Step 4:

• Train the model by providing training dataset as input to the model.

Step 5:

• Once the model is trained with training data, it can be used/consumed to predict the classes/Labels of test dataset or new data samples.