Statistical Redundancy
Statistical redundancy refers to the presence of excessive or unnecessary information in a dataset that can negatively impact the performance of machine learning models. This can occur when variables in a dataset are highly correlated or when there are too many variables relative to the number of observations. Redundant variables can lead to overfitting, where a model performs well on the training data but poorly on new, unseen data. To avoid statistical redundancy, feature selection techniques can be used to identify and remove redundant variables from the dataset. These techniques include correlation analysis, principal component analysis (PCA), and regularization methods such as Lasso and Ridge regression.
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