Sebastian Raschka • 6/11/2016

Model evaluation, model selection, and algorithm selection in machine learning

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This article explores techniques for evaluating machine learning models, estimating their generalization performance, and selecting the best model or algorithm. It addresses the importance of moving beyond simple training accuracy to ensure models perform well on unseen data, covering topics like hyperparameter tuning and performance estimation for effective model comparison.

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#Machine Learning #Model Selection #Model Evaluation
Model evaluation, model selection, and algorithm selection in machine learning

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