Multicollinearity

54 min
Block 4 — Understanding the data before modeling
Objective
define collinearity and multicollinearity rigorously, separate the perfect case from the approximate one, state the condition in terms of the rank of the design matrix, trace the consequences through to unstable coefficients and sign flips, detect the problem with the correlation matrix, the variance inflation factor and the condition number, choose a treatment knowing what it costs, and arbitrate according to whether the model is built to explain or to predict.
Estimated duration
45 minutes
Prerequisites
chapters 006 (features and target), 014 (descriptive statistics) and 016 (correlation and causation)
Associated quizzes
017.1-quiz-collinearity-definition.md to 017.6-quiz-interpretation-vs-prediction.md

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This lesson is part of the “Exploratory Data Analysis (EDA)” module

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