Statistical Prediction
Multicollinearity refers to a situation in regression analysis where two or more predictor variables are highly correlated, making it difficult to determine their individual effects on the response variable. This issue can lead to unreliable and unstable coefficient estimates, increasing the standard errors and complicating the interpretation of the model. It is particularly relevant in regression models, as it can inflate variance and affect the performance of the model, necessitating techniques such as L2 regularization to mitigate its impact.
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