Effective techniques for validating machine learning models include cross-validation, stratified sampling, and the use of validation sets. Cross-validation involves partitioning the dataset into subsets, training the model on some subsets while testing on the others to ensure robustness. Stratified sampling ensures that each subset has a representative distribution of classes. The use of validation sets allows for the assessment of model performance on unseen data, preventing overfitting. Implementing these techniques results in more reliable and accurate machine learning models.
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