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Mastering Probability Distributions for Machine Learning
Probability distributions are maps of data behavior. Understanding them allows you to select better models, engineer features effectively, and quantify uncertainty in production pipelines.
Python in Plain English
Improving Uncertainty Estimation for Classifier Performance
Standard confidence interval methods often fail for small datasets or high-performance models; using Agresti-Coull, Wilson, or regularized bootstrap methods significantly improves accuracy.
arXiv cs.AI
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