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What is the purpose of hyperparameter tuning?
Practice Questions
Q1
What is the purpose of hyperparameter tuning?
To select the best features
To improve model performance by optimizing parameters
To evaluate model accuracy
To visualize data distributions
Questions & Step-by-Step Solutions
What is the purpose of hyperparameter tuning?
Steps
Concepts
Step 1: Understand that a model is a set of rules that helps us make predictions based on data.
Step 2: Know that hyperparameters are settings that we can adjust before training the model, like learning rate or number of layers.
Step 3: Realize that tuning these hyperparameters means changing them to find the best combination.
Step 4: Recognize that the goal of tuning is to make the model perform better, meaning it makes more accurate predictions.
Step 5: Conclude that hyperparameter tuning is important for getting the best results from a machine learning model.
No concepts available.
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