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Find-S algorithm is a Machine Learning Algorithm that finds the most specific hypothesis that fits all the positive examples.

Project description

Find-S Algorithm

Find-S algorithm is a Machine Learning Algorithm that finds the most specific hypothesis that fits all the positive examples.


Installation

Install directly from my PyPi

pip install classic-FindS

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

1. Install the package

pip install classic_FindS

2. Import the library

from classic_FindS import FindS

3. Create an object for FindS class

fs = FindS()

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 Find-S Library

  • from classic_FindS import FindS
  • fs = FindS()
  • S_hypothesis = fs.fit(X_train, y_train)
  • print("Specific Hypothesis : ", S_hypothesis)
  • y_pred = fs.predict(X_test)

Footnotes

You can find the code at my Github.

Connect with me on Social Media

Project details


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