Bias and variance provide a powerful conceptual tool for analyzing machine learning performance. My research revealed that as sample size increases, the ratio of variance relative to bias tends to decrease. As has since become widely accepted, this implies that low variance learning algorithms, such as linear models, should be most effective with small data quantity. For large data quantities, low bias learning algorithms, such as deep learning, should be most effective.
Previous approaches to conducting bias-variance experiments have provided little control over the types of data distribution from which bias and variance are estimated. I have developed new techniques for bias-variance analysis that provide greater control over the data distribution. Experiments show that the type of distribution used for bias-variance experiments can greatly affect the results obtained.
Publications
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