Multicollinearity Impacts Your Data Science Project More Than You Know

Multicollinearity is likely far down on a mental list of things to check for, if it is on a list at all. This does, however, appear almost always in real-life datasets, and it’s important to be aware of how to address it.

Multicollinearity is likely far down on a mental list of things to check for, if it is on a list at all. This does, however, appear almost always in real-life datasets, and it’s important to be aware of how to address it.

As its name suggests, multicollinearity is when two (or more) features are correlated with each other, or ‘collinear’. This occurs often in real datasets, since one measurement (e.g. family income) may be correlated with another (e.g. school performance). You may be unaware that many algorithms and analysis methods rely on the assumption of no multicollinearity.

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