Hyperparameter Tuning with Tune

Hyperparameters are adjustable parameters that govern the training process of a model but are not learned from the data itself. These include settings like the number of trees in a random forest, the maximum depth of a decision tree, or the penalty term in regularized regression. Selecting appropriate hyperparameter values is crucial because they can significantly influence model performance. If set too high or too low, hyperparameters may cause overfitting or underfitting, leading to poor generalization on new data.

During hyperparameter tuning, it is important to use cross-validation to estimate model performance for each hyperparameter combination. This helps avoid overfitting to a single train-test split and provides a more robust assessment of how the model will perform on unseen data. Once tuning is complete, you can select the best model based on the performance metric most relevant to your problem, such as accuracy for classification or RMSE for regression. The selected model can then be finalized and retrained on the entire training set before being evaluated on the test set.