Bias-Variance Tradeoff Illustration Using Pylab
Last Updated on July 25, 2023 by Editorial Team
Author(s): Benjamin Obi Tayo Ph.D.
Originally published on Towards AI.
In statistics and machine learning, the bias-variance tradeoff is the property of a set of predictive models whereby models with a lower bias in parameter estimation have a higher variance of the parameter estimates across samples and vice versa. The bias-variance dilemma or problem is the conflict in trying to simultaneously minimize these two sources of error that prevent supervised learning algorithms from generalizing beyond their training set:
The bias is an error from erroneous assumptions in the learning algorithm. High bias can cause an algorithm to miss the relevant relations between features and target outputs (underfitting).The variance is an error… Read the full blog for free on Medium.
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