Understanding the Model Evaluation and Selection Techniques Content of Model Evaluation • Model Performance Metrics • Cross-Validation Techniques • Hyperparameter Tuning • Model Selection Techniques Model Evaluation and Selection: Model evaluation and selection is the process of choosing the best machine learning model based on its performance on a given dataset. There are several techniques for evaluating and selecting machine learning models, including performance metrics, cross-validation techniques, hyperparameter tuning, and model selection techniques. Performance Metrics: Performance metrics are used to evaluate the performance of a machine learning model. The choice of performance metric depends on the specific task and the type of machine learning model being used. Some common performance metrics include accuracy, precision, recall, F1 score, ROC curve, and AUC score. Cross-Validation Techniques: Cross-validation is a technique used to evaluate the per
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