Linear Regression Assumptions Violated

Medium

machine-learning Two Sigma JPMorgan Capital One

Multiple Choice

Linearity, because the relationship between features and target is non-linear Homoscedasticity, because the variance of residuals changes with the predicted value Normality of residuals, because the residuals are not bell-shaped Independence, because residuals are correlated with each other

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