What are the best practices for using statistical learning to improve feature selection models?

Do try the brilliant PPS (Predictive Power Score)! There is a package implementing it both in R and Python. A massive improvement on using correlation for univariate feature selection :3

So I am always of a fan of a correlation matrix and also do some sort of projection work like PCA. I admit that there is always the danger that your projection can be orthogonal to the outcome you are trying to predict. For feeding into treebased models I often use something like a random forest to select the features to then feed into the actual model. I for linear models I often use glmnet to do the same exercise.

Filter methods involve evaluating the relevance of features independently of any machine learning algorithm. These methods typically use statistical tests or correlation metrics to rank features based on their individual characteristics. Features are selected or eliminated before applying the learning algorithm, making filter methods computationally efficient and less prone to overfitting. However, they may overlook interactions between features and fail to consider the model's performance directly.

Filter methods efficiently select features based on statistical measures like correlation or variance, simplifying feature selection. However, they overlook feature interactions and model bias, potentially leading to suboptimal performance. By following best practices such as choosing suitable measures and conducting exploratory analysis, their effectiveness can be enhanced, though they may not address all complexities in feature selection.

To optimize feature selection models with statistical learning, ensure relevance by choosing suitable measures like correlation or entropy. Apply thresholds for filtering and conduct exploratory data analysis for insights. Use multiple methods for validation to enhance model performance.

Wrapper methods choose feature subsets by testing their performance with a particular machine learning algorithm. They assess various combinations of features to determine their impact on the model's effectiveness. While wrapper methods offer a thorough approach by considering feature interactions directly, they are more computationally demanding than filter methods. Despite their potential to yield superior feature subsets, they can be prone to overfitting, particularly with small datasets. Additionally, wrapper methods may struggle to handle high-dimensional feature spaces efficiently.

Feature selection in machine learning is like building a winning sports team. Just as you'd pick players who complement each other's strengths, wrapper methods tirelessly test different feature combinations to maximize model performance. However, this process can be computationally intensive and risk overfitting. To optimize, choose appropriate models, employ strategies like forward selection, and validate using techniques like cross-validation. Regularization methods act like seasoned coaches, ensuring fair play among features while enhancing performance.

Wrapper methods in feature selection test different combinations of features by training and testing the model with each one. They focus on how well the model performs with each set of features, which can lead to better selections. However, they can be slow because they train the model many times. Still, they often give good results by picking the best features for the model.

Wrapper methods in feature selection involve using models to assess feature importance, capturing interactions but at the cost of computational complexity. To optimize, choose appropriate algorithms like forward selection and validate with techniques such as cross-validation. Regularization methods like lasso can mitigate overfitting and improve generalization. ?????

While working with a large number of features or sophisticated models, wrapper approaches may be computationally costly and time-consuming.

Embedded methods integrate feature selection directly into the model training process. These methods select features as part of the model's learning algorithm, optimizing both feature selection and model fitting simultaneously. Embedded methods are efficient and can effectively handle high-dimensional data, as they consider feature relevance within the context of the model's predictive performance. However, they may be less flexible compared to wrapper methods in terms of feature selection strategies and can be computationally demanding depending on the complexity of the learning algorithm.

Embedded methods merge the strengths of filter and wrapper methods, offering efficient feature selection within models. While they balance bias and variance, they lack transparency and may be model-specific. Optimal use involves selecting appropriate models, tuning parameters, and validating results for robustness. ???????

Embedded methods automatically select features during model training & offer a balance between wrapper and filter methods. These are less costly compared to wrapper methods but may not consider feature interactions as effectively. Common embedded methods are - (1) REGULARIZATION for Linear Regression Models (a) Lasso regression(L1 Regularization) : Picks important features, discards less important ones. (b) Ridge regression(L2 Regularization) : Keeps all features, shrinks coefficients(reducing the impact of features). (2) PRUNING for Decision Trees : Trims unnecessary branches(removes less important features) (3) SPLITTING for Decision Trees and Ensemble Methods(RF & GBM) : Divides the data into groups that are similar in some way.

Embedded methods for feature selection: Selection While Training: These methods pick features as the model learns. Depend on Model: They use the model's process to choose features. Automatic Choice: Features are automatically selected based on their importance for the model. Regularization: They often use techniques like Lasso or Ridge to help choose features. Efficient: Embedded methods save time by selecting features during model training. Integrated Approach: Feature selection is part of the model-building process, reducing complexity. Based on Model Performance: Features are chosen based on how well they improve model performance. Embedded methods are efficient and choose features automatically as the model learns.

Leverage Regularization: Use models with built-in regularization such as LASSO (L1 regularization) or Elastic Net, which can shrink the coefficients of less important features to zero, effectively performing feature selection. Model-Specific Importance: Utilize models like Decision Trees or Random Forests, which provide feature importance scores based on how features improve the purity of the split. These scores can guide the selection of the most relevant features. Balance Complexity and Performance: When using embedded methods, carefully tune regularization parameters to balance the complexity of the model with predictive performance, ensuring you don’t oversimplify the model by removing too many features.

Domain Knowledge: Involve domain experts to identify potentially important features that statistical tests might miss. Data Preprocessing: Handle missing values, outliers, and scaling issues before feature selection. Interpretability vs. Performance: Consider the trade-off between black-box models with high accuracy and simpler models that are easier to explain.

I'd also look at dimensionality reduction methods like PCA and tSNE for feature reduction and using domain knowledge to create new features (like transformed or lagged variables in a regression model) Another approach for smart feature selection in models is to measure feature importance using SHAP (Shapley Additive Explanations) like methods which can rank the importance/explainability that each feature has on the model and that takes interaction effects into account.

Consider dimensionality reduction techniques like Principle component Analysis (PCA), Independent Component Analysis (ICA), Linear Discriminant Analysis (LDA) and select relevant features.