Probabilistic Decision-Making
Lasso regression is a type of linear regression that incorporates L1 regularization to improve the model's prediction accuracy and interpretability by shrinking some coefficients to zero. This technique is particularly useful in situations where there are many predictors, as it effectively selects a simpler model by penalizing the absolute size of the coefficients, thus reducing the risk of overfitting. By connecting this method to the analysis of multiple variables, lasso regression helps in understanding how each predictor influences the outcome while keeping the model manageable.
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