What is the role of hyperparameter tuning in model selection?

What is the role of hyperparameter tuning in model selection?

Step 1: Understand that a model is a mathematical representation used to make predictions.
Step 2: Know that hyperparameters are settings that you can adjust before training the model, like learning rate or number of trees in a forest.
Step 3: Realize that tuning these hyperparameters means trying different values to see which ones help the model perform better.
Step 4: Recognize that the goal of hyperparameter tuning is to improve the model's accuracy and effectiveness on new data.
Step 5: Remember that selecting the best hyperparameters is part of the model selection process, helping you choose the right model for your data.

No concepts available.