Numerical_Feature_Selection

in this section will be numerical feature selection on diabetes dataset

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Feature Selection

Feature selection is the process of selecting a subset of relevant features from a larger set to improve model performance or reduce computational complexity. Types of feature selection refers to various methodologies or approaches used to select a subset of features from the original feature set. These methodologies can be broadly categorized into three main types:

• Filter Methods
• Wrapper Methods
• Embedded Methods

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Numerical Feature Selection

• Filter Methods

  1. Mutual Information: Measures the relationship between two random variables; assesses the information gain by measuring the relationship of features with the target variable.

  2. Chi-Squared: Measures the relationship between categorical independent variables and a categorical target variable; tests the association between two categorical variables.

  3. f_classif: is a feature selection method commonly employed to evaluate the influence of features on a classification task, often utilizing ANOVA (Analysis of Variance). ANOVA is utilized to determine the statistical significance of the relationship between each feature and the target variable. High F-statistic values resulting from ANOVA imply a substantial association between the feature and the target, indicating its importance for classification. Conversely, low F-statistic values suggest a weaker impact. This technique plays a crucial role in feature selection processes aimed at enhancing model performance by selecting the most informative features.

  4. ROC_AUC: Calculates the area under the Receiver Operating Characteristic (ROC) curve (AUC), evaluating classifier performance; a higher ROC AUC value indicates better classifier performance.

  5. Correlation: Measures the linear relationship between features; assesses the correlation between features using statistical metrics such as the Pearson correlation coefficient.