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Machine Learning Requires Probability and Statistics
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DOI：10.1109/MSP.2020.2985385.png)
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Abstract
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Machine Learning Requires Probability and Statistics The contemporary practice of machine learning often involves the application of deterministic, computationally intensive algorithms to iteratively minimize a criterion of fit between a discriminant and sample data. There is often little interest in using probability to model the uncertainty in the problem and statistics to characterize the behavior of predictors derived from data, with the emphasis being on computation and coding. It follows that little can be stated about performance on future data, beyond perhaps a simple error count on a given test set. In this article, we argue that the knowledge imparted by deterministic computational methods is not rigorously related to the real world and, in particular, future events. This connection requires rigorous probabilistic modeling and statistical inference as well as an understanding of the proper role of computation and an appreciation of epistemological issues.
Keywords:
Bayes methods
Machine learning
Biological neural networks
Error analysis
Training data
Predictive models
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