6 b] Explain different selecting criterion in feature selection.
When performing feature selection in machine learning, various selection criteria are used to evaluate and select the most relevant features for the model. Here’s a brief overview of some of the most common selection criteria:
1. R-squared (R²)
- Purpose: Measures the proportion of variance in the dependent variable that is explained by the independent variables.
- Formula:
- Interpretation: A higher R² value indicates a better fit of the model to the data, as it explains more variance.
2. p-values
- Purpose: Tests the statistical significance of each feature’s coefficient in a regression model.
- Interpretation: A low p-value (< 0.05, typically) suggests that the feature has a statistically significant contribution to the model, meaning the feature’s effect is unlikely due to random chance.
3. Akaike Information Criterion (AIC)
- Purpose: Balances model fit and complexity by penalizing the number of parameters.
- Formula: ( AIC = 2k−2ln L )
- Interpretation: Lower AIC values are better, indicating a model that achieves a good fit with fewer parameters.
4. Bayesian Information Criterion (BIC)
- Purpose: Similar to AIC, but adds a heavier penalty for the number of parameters, especially when the dataset is large.
- Formula: ( BIC = k*ln n −2ln L , )
- Interpretation: Like AIC, lower BIC values indicate a better model, but BIC penalizes complexity more heavily.
5. Entropy
- Purpose: Measures the impurity or disorder within a dataset, often used in decision trees for feature selection.
- Interpretation: Lower entropy indicates a more informative feature, as it reduces uncertainty about the target variable.
Each selection criterion balances model fit, complexity, and statistical significance in different ways, and the choice of criterion depends on the specific problem, dataset, and model being used.