Model Selection
Model selection is the process of choosing the best model from a set of candidate models that best fits the data and generalizes well to new data. In data science and artificial intelligence, model selection is a crucial step in the machine learning pipeline. It involves evaluating different models based on their performance metrics such as accuracy, precision, recall, F1 score, and AUC-ROC. Model selection can be done using various techniques such as cross-validation, grid search, and Bayesian optimization. The goal of model selection is to find the best model that balances the trade-off between underfitting and overfitting. Underfitting occurs when the model is too simple and cannot capture the complexity of the data, while overfitting occurs when the model is too complex and fits the noise in the data. Model selection helps to prevent overfitting and improve the generalization performance of the model.
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