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

# 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

## * X_train

The Training Set array consisting of Features.

## * y_train

The Training Set array consisting of Outcome.

## * 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

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
• 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.