Model Selection Process
Model selection process is the process of selecting the best model from a set of candidate models that best describes the relationship between the variables in a dataset. The process involves evaluating different models based on their performance metrics such as accuracy, precision, recall, F1 score, and AUC-ROC. The goal of the model selection process is to choose a model that generalizes well on unseen data and avoids overfitting or underfitting. The process involves splitting the dataset into training, validation, and test sets, and using techniques such as cross-validation, grid search, and hyperparameter tuning to evaluate and compare the performance of different models. The model selection process is a crucial step in the machine learning pipeline and can significantly impact the performance of the final model.
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