Filter Method for Feature Selection Using Statistical Tests

The correct answer is B. Filter Method. Here's why:

Filter methods in feature selection operate independently of any specific machine learning algorithm. They rely on statistical tests to assess the relationship between each feature and the target variable. Here's how it works:

1. Statistical Tests: Various statistical tests are employed to measure the correlation or dependence between each feature and the target variable. Common tests include: * Chi-squared test: For categorical features. * ANOVA (Analysis of Variance): For continuous features with a categorical target. * Correlation coefficient (Pearson's r): For continuous features.

2. Ranking and Thresholding: Features are ranked based on the scores obtained from these tests. A predefined threshold is set. Features scoring below the threshold are considered weakly correlated with the target and are removed, while those above the threshold are selected.

Key Advantages of Filter Methods:

• Computational Efficiency: They are generally faster than wrapper methods, especially with high-dimensional datasets.* Model Agnostic: They work independently of the chosen machine learning algorithm, making them versatile.

In contrast, Wrapper Methods (option A):

• Involve training the machine learning model with different subsets of features.* Evaluate model performance to determine the best feature subset. * Are computationally more expensive but can potentially lead to slightly better performance.