Elastic Net is a regularization technique that linearly combines the penalties of L1 (Lasso) and L2 (Ridge) regularization to enhance model accuracy and interpretability, especially when dealing with highly correlated features. It is particularly useful in scenarios where the number of predictors exceeds the number of observations or when there are groups of correlated variables.

Elastic Net

regularization technique

penalties of L1

LASSO, which stands for Least Absolute Shrinkage and Selection Operator, is a regression analysis method that performs both variable selection and regularization to enhance the prediction accuracy and interpretability of statistical models. It achieves this by imposing a constraint on the sum of the absolute values of the model coefficients, effectively shrinking some coefficients to zero and thereby selecting a simpler model that avoids overfitting.

Lasso

Ridge

Model accuracy is a metric used to evaluate the performance of a predictive model by calculating the proportion of correct predictions made out of all predictions. It is crucial for determining how well a model generalizes to unseen data but should be considered alongside other metrics to avoid misleading interpretations, especially in imbalanced datasets.

model accuracy

Interpretability is the degree to which a human can understand the cause of a decision made by a model or system, crucial for trust and accountability in AI and machine learning applications. It enables stakeholders to validate models, ensure fairness, and comply with regulatory standards by providing insights into how inputs are transformed into outputs.

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