Candidate-Elimination Algorithm

Candidate-Elimination Algorithm is a Machine Learning Algorithm that builds the version space from Specific Hypothesis and General Hypothesis.

Installation

Install directly from my PyPi

pip install classic-CandidateElimination

Or Clone the Repository and install

python3 setup.py install

Parameters

* X_train

The Training Set array consisting of Features.

* y_train

The Training Set array consisting of Outcome.

Attributes

* fit(X_train, y_train)

Fit the Training Set to the model.

* predict(y_test)

Predict the Test Set Results.

Documentation" class="reference-link"> Documentation

1. Install the package

pip install classic_FindS

2. Import the library

from classic_CandidateElimination import Candidate_Elimination

3. Create an object for FindS class

ce = Candidate_Elimination()

4. Fit your Training Set to the model

fs.fit(X_train, y_train)

5. Predict your Test Set results

y_pred = fs.predict(y_test)

Example Code

1. Import the dataset and Preprocess

import numpy as np
import pandas as pd
dataset = pd.read_csv(‘Covid-19_Data.csv’)
result = {‘Yes’:1, ‘No’:0}
dataset[‘Covid_19’] = dataset[‘Covid_19’].map(result)
X = dataset.iloc[:, 0:5].values
y = dataset.iloc[:, -1].values

from sklearn.model_selection import KFold

kf = KFold(n_splits=10)
for train_index, test_index in kf.split(X,y):

X_train, X_test = X[train_index], X[test_index]
y_train, y_test = y[train_index], y[test_index]

2. Use the Candidate Elimination Library

from classic_CandidateElimination import Candidate_Elimination
ce = Candidate_Elimination()
ce.fit(X_train, y_train)
y_pred = ce.predict(X_test)

Footnotes

You can find the code at my Github.