classic-CandidateElimination 2.0.0

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

Candidate-Elimination Algorithm

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

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Installation

Install directly from my PyPi

pip install classic-CandidateElimination

Or Clone the Repository and install

python3 setup.py install

Parameters

* X_train

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The Training Set array consisting of Features.

* y_train

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The Training Set array consisting of Outcome.

Attributes

* fit(X_train, y_train)

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Fit the Training Set to the model.

* predict(y_test)

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Predict the Test Set Results.

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)

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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 Find-S Library

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

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Footnotes

You can find the code at my Github.

Connect with me on Social Media

• https://www.github.com/safir72347
• https://www.linkedin.com/in/safir72347/
• https://www.instagram.com/safir_12_10/