Feature selection is also known as attribute selection is a process of extracting the most relevant features from the dataset and then applying machine learning algorithms for the better performance of the model. A large number of irrelevant features increases the training time exponentially and increase the risk of overfitting.
Chi-square Test for Feature Extraction:
Chi-square test is used for categorical features in a dataset. We calculate Chi-square between each feature and the target and select the desired number of features with best Chi-square scores. It determines if the association between two categorical variables of the sample would reflect their real association in the population.
Chi- square score is given by :
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 of Chi-Square feature selection:
# Load libraries from sklearn.datasets import load_iris from sklearn.feature_selection import SelectKBest from sklearn.feature_selection import chi2   # Load iris data iris_dataset = load_iris()   # Create features and target X = iris_dataset.data y = iris_dataset.target   # Convert to categorical data by converting data to integers X = X.astype(int)   # Two features with highest chi-squared statistics are 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]) |
Output:
Original feature number: 4 Reduced feature number : 2
