InformationGain 3.0.0

A Python package for calculating information gain.

informationGain Library

This module helps you calculate Information Gain for both categorical and continuous features.

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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	If True, assumes the first column is an index and skips it	True
criteria	String	Splitting criteria: "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.
• Handles both categorical and continuous features:
  • For categorical features, splits data by unique values.
  • For continuous features, finds the best threshold (value) that gives maximum Information Gain.
• Supports Gini Index or Entropy as the impurity metric.
• Returns a DataFrame with:
  • Feature name
  • Best threshold (or 'none' for categorical)
  • Corresponding Information Gain

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Returns

A pandas.DataFrame with columns:

• 'feature': Name of the feature
• 'threshold': Best threshold value (for continuous features), or 'none'
• 'infogain': Calculated Information Gain

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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:
#     feature  threshold   infogain
# 0   outlook      none    0.246750
# 1      temp      72.5    0.029223
# 2  humidity      80.0    0.151836
# 3      wind      none    0.048127