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A comment on Machine Learning Crash Course: The Bias-Variance Dilemma. Parent comment

Re:Machine Learning Crash Course: The Bias-Variance Dilemma by tedsanders on Tuesday July 18, 2017 @03:28AM (#14794856)
Honestly, the nonlinear model looks far better to my eye than the linear model. The error term of the linear model seems obviously dependent on X, which contradicts the notion that the linear model was "correct". I think the article does a disservice to the reader by oversimplifying and calling the linear model "correct".
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Re:Machine Learning Crash Course: The Bias-Variance Dilemma by abecedarius on Tuesday July 18, 2017 @10:41AM (#14796873)
As therajiv pointed out, there were only a handful of data points supporting the kink in the curve, down at its end, versus lots and lots for the main line. Even in freshman physics labs you get told it's a terrible idea to extrapolate from a few points at the end of a curve, because those are typically the noisiest.
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Re:Machine Learning Crash Course: The Bias-Variance Dilemma by tedsanders on Tuesday July 18, 2017 @11:19AM (#14797200)
Certainly. But the issues of a linear model being "correct" and how to extrapolate forward are not the same.
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