How Overfitting Ruins Your Feature Selection

Overfitting can negatively impact the performance of machine learning models by causing them to learn noise and irrelevant information from the training data, leading to poor generalization on unseen data. This issue is particularly problematic in feature selection, where overfitting can lead to inconsistent feature importance rankings, discarding relevant features, selecting irrelevant features, increased sensitivity in data variability, and poor generalization. To prevent overfitting, regularization techniques, cross-validation, and ensemble methods can be employed. Regularization adds a penalty term to the loss function, discouraging the model from fitting too closely to the training data. Cross-validation divides the original training dataset into mini-train and test splits, allowing for better hyperparameter tuning and assessment of model performance on unseen data. Ensemble methods combine predictions from multiple base models, capturing diverse perspectives and reducing individual model biases, leading to improved generalization and more stable feature importances.