How can I use advanced techniques in scikit-learn pipelines?

How can I use advanced techniques in scikit-learn pipelines?

You can enhance your scikit-learn pipelines by incorporating techniques like feature selection, custom transformers, and grid search for hyperparameter tuning.

Can you explain how to implement a custom transformer?

Sure! You can create a custom transformer by subclassing BaseEstimator and TransformerMixin. Implement the fit and transform methods to define your transformation logic.

What are some common use cases for feature selection in pipelines?

Common use cases include reducing overfitting, improving model performance, and decreasing training time by selecting only the most relevant features.

How do I integrate grid search into my pipeline?

You can use GridSearchCV from scikit-learn. Wrap your pipeline in it and specify the parameters you want to tune. It will automatically handle cross-validation.