InformationGain 2.1.0

A Python package for calculating information gain.

informationGainLibrary

This module helps you calculate Information Gain for categorical data.

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Class

infoGain.calculate(data, target, criteria="gini", fIndex=True)

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Parameters

Parameter	Type	Description	Default
data	DataFrame	Dataset in pandas dataframe format	Required
target	String	Output column (the target variable)	Required
fIndex	Boolean	Specifies if the first column of the dataset is an index column	True
criteria	String	Splitting criteria to use: "gini" or "entropy"	"gini"

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Description

• Calculates Information Gain for each feature/column in the dataset with respect to the target column.
• Useful for decision tree splitting based on entropy.
• Returns: A dictionary where keys are feature/column names and values are their corresponding Information Gain with respect to the target column.

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Example Usage

from informationGain import infoGain

import pandas as pd

# Load dataset
data = pd.read_csv('your_dataset.csv')

# Initialize
ig = infoGain()

# Calculate Information Gain
result = ig.calculate(data, target='Output', fIndex=True)

print(result)
# Example Output:
# {'Feature1': 0.25, 'Feature2': 0.18, 'Feature3': 0.0}