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