Data Splitting and Cross-Validation Strategies. An Essential Guide for Model Evaluation.
Index. 1. Header. 2. Index. 3. Purpose of Data Splitting.
Purpose of Data Splitting. Data splitting ensures unbiased model evaluation and prevents overfitting by creating distinct datasets for different phases of machine learning..
Cross-Validation Techniques. Cross-validation provides robust model evaluation by using multiple train-test splits to assess performance consistency..
Benefits and Trade-offs. Benefits. More reliable performance estimates.