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This might be a basic question: What is the difference between bias and underfitting? And, analogously, what is the difference between variance and overfitting? Do the terms of each pair mean the same thing? If not, what is the difference?

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They do not exactly mean the same thing, but they are correlated in the following manner:

  • Over fitting occurs when the model captures the noise and the outliers in the data along with the underlying pattern. These models usually have high variance and low bias. These models are usually complex like Decision Trees, SVM or Neural Networks which are prone to over fitting.

  • Under fitting occurs when the model is unable to capture the underlying pattern of the data. These models usually have a low variance and a high bias. These models are usually simple which are unable to capture the complex patterns in the data like Linear and Logistic Regressions.

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    $\begingroup$ You give examples, but don't explain what the exact difference is. Could you please elaborate? Thank you! $\endgroup$
    – Funkwecker
    Nov 28, 2019 at 7:23

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