How do you approach model selection and hyperparameter tuning?
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Answer / Satish Chadhary
Model selection involves choosing the best model for a given task based on its performance on validation data. Common approaches include cross-validation and grid search. Hyperparameter tuning is the process of adjusting the parameters of a machine learning algorithm to improve its performance. This can be done manually or using automated methods like Bayesian optimization.
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