How can I optimize my machine learning model?
To optimize your model, you can start by tuning hyperparameters, selecting the right features, and using cross-validation.
What are hyperparameters and how do I tune them?
Hyperparameters are settings that you configure before training your model. You can tune them using techniques like grid search or random search.
Can you explain feature selection?
Feature selection involves choosing the most relevant features for your model to improve performance and reduce overfitting.
What is cross-validation and why is it important?
Cross-validation is a technique to assess how the results of a statistical analysis will generalize to an independent dataset. It's important for ensuring your model's robustness.
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