Cross-validation: 100 Tips & Strategies for Robust Model Assessment

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Cross-validation is a crucial technique in machine learning and statistics to assess the performance and generalization ability of a predictive model. Here are 100 tips for cross-validation:

1. Basics of Cross-Validation:

1. Understand Cross-Validation (CV): Cross-validation is a resampling technique used to evaluate machine learning models by training and testing on multiple subsets of the dataset.
2. K-Fold Cross-Validation: Commonly used technique where the dataset is divided into k folds, and the model is trained and tested k times.
3. Stratified Cross-Validation: Maintain the class distribution in each fold for imbalanced datasets.
4. Leave-One-Out Cross-Validation (LOOCV): Special case of k-fold where k equals the number of samples.
5. Nested Cross-Validation: Use nested cross-validation for hyperparameter tuning and model selection.

2. Model Evaluation Metrics:

1. Select Appropriate Metric: Choose evaluation metrics (e.g., accuracy, precision, recall, F1-score) based on the nature of the problem.
2. Use Mean Squared Error (MSE): For regression problems, use MSE as an evaluation metric.