Machine Learning Fundamentals: Bias and Variance
Bias and Variance are two basic ideas for Machine Studying, and their instinct is just a bit completely different from what you might need realized in your statistics class. Right here I’m going by means of two examples that make these ideas tremendous straightforward to know.
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0:00 Superior tune and introduction
0:29 The information and the “true” mannequin
1:23 Splitting the info into coaching and testing units
1:40 Least Regression match to the coaching knowledge
2:16 Definition of Bias
2:33 Squiggly Line match to the coaching knowledge
3:40 Mannequin efficiency with the testing dataset
4:06 Definition of Variance
5:10 Definition of Overfit
4:06 I say that the distinction in suits between the coaching dataset and the testing dataset is named Variance. Nevertheless, I ought to have mentioned that the distinction is a _consequence_ of variance. Technically, variance refers back to the quantity by which the predictions would change if we match the mannequin to a distinct coaching knowledge set.
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