Mathematical Probability Theory
Lasso regression is a type of linear regression that includes a regularization term to prevent overfitting and enhance the model's prediction accuracy. By adding a penalty equal to the absolute value of the magnitude of coefficients, lasso regression encourages sparsity in the model, effectively selecting a simpler model that can improve interpretability and performance. This method is particularly useful when dealing with high-dimensional datasets where the number of predictors exceeds the number of observations.
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