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One thing people often ask is whether adding too many features is a problem. And the honest answer is yes, if you do not check for multicollinearity. When features are too similar to each other, they carry the same information and can distort your model’s interpretation and stability. That is why checking correlations, VIF, or feature redundancy matters. You want signals, not echoes. More features does not automatically mean better models. Clean, independent features almost always beat a long li
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One thing people often ask is whether adding too many features is a problem. And the honest answer is yes, if you do not check for multicollinearity. When features are too similar to each other, they carry the same information and can distort your model’s interpretation and stability. That is why checking correlations, VIF, or feature redundancy matters. You want signals, not echoes. More features does not automatically mean better models. Clean, independent features almost always beat a long li
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