Data leakage occurs when you inadvertently include knowledge from testing data when training a Machine Learning model.
Data leakage is problematic as it makes model evaluation scores less reliable, leading to potential bad decisions and overestimation of model performance on new data.
Imputing missing values in pandas before passing data to scikit-learn can result in data leakage, impacting model evaluation reliability.
To prevent data leakage, perform all data transformations within scikit-learn, including missing value imputation, to ensure accurate estimation of model performance on new data.
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