ML | Chi-square test for function selection

Michael Zippo

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Chi-square test for feature extraction:
Chi-square test is used for categorical features in the dataset. We calculate the chi-square between each object and target and select the desired number of objects with the best chi-square values. It determines whether the relationship between two categorical variables in the sample will reflect their actual relationship in the population.
Chi-square score is given:

where —

Observed frequency = No. of observations of class
Expected frequency = No. of expected observations of class if there was no relationship between the feature and the target.

Python Implementation for Chi-Square Feature Selection:

# Download libraries

from sklearn.datasets import load_iris

from sklearn.feature_selection import SelectKBest

from sklearn.feature_selection import chi2

# Loading iris data

iris_dataset = load_iris ()

# Create functions and goals

X = iris_dataset.data

y = iris_dataset.target

# Convert to categorical data by transforming data to integers

X = X. astype ( int )

# Two features with the highest chi-square statistics selected

chi2_features = SelectKBest (chi2, k = 2 )

X_kbest_features = chi2_features.fit_transform (X, y)

# Reduced features

print ( ’Original feature number:’ , X.shape [ 1 ])

print ( ’Reduced feature number:’ , X_kbest.shape [ 1 ])

Exit :

 Original feature number: 4 Reduced feature number: 2

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Prague | 2023-01-29

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