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Difference between Bias and Variance

Ruthvik Raja M.V
Masters in Computer Engineering at University of Guelph, Canada.
Updated on ・1 min read

Consider bias as error in Training Data and variance as error in Test Data.

Under fitting model:
High Bias and Low Variance [If you try to fit a simple model such that most of the training data points won’t be satisfied].

Over fitting model:
Low Bias and High Variance [If you try to fit a model such that most of the training data points would be exactly satisfied].

So, it is very important to build a Perfect Model such that it satisfies most of the training data points and gives better results for the test data [Low Bias and Low Variance].

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