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ML Academy

Introduced on day 11

Multicollinearity

Strong correlation among inputs that makes individual coefficients unstable, even though predictions on similar data can remain stable.

In detail

When two columns carry nearly the same information, many coefficient pairs fit equally well, so estimates swing between resamples and signs can flip. Only their combined effect is identified. Check pairwise correlations or variance inflation factors; remedies are dropping or combining columns, domain-driven aggregation or ridge shrinkage. It matters for interpretation more than for prediction.