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What is the purpose of hyperparameter tuning in model selection?
Practice Questions
Q1
What is the purpose of hyperparameter tuning in model selection?
To adjust the model's architecture
To select the best features
To improve model performance
To visualize results
Questions & Step-by-Step Solutions
What is the purpose of hyperparameter tuning in model selection?
Steps
Concepts
Step 1: Understand that a model has settings called hyperparameters that need to be set before training.
Step 2: Realize that these hyperparameters can affect how well the model learns from the data.
Step 3: Know that hyperparameter tuning is the process of finding the best values for these settings.
Step 4: Use techniques like grid search or random search to test different combinations of hyperparameters.
Step 5: Evaluate the model's performance with each combination to see which one works best.
Step 6: Choose the combination of hyperparameters that gives the best results for making predictions.
No concepts available.
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