How can I use advanced features of scikit-learn pipelines?

How can I use advanced features of scikit-learn pipelines?

You can leverage advanced features like custom transformers, pipeline composition, and grid search for hyperparameter tuning.

Can you give me an example of a custom transformer?

Sure! A custom transformer can be created by subclassing BaseEstimator and TransformerMixin. For instance, a transformer that scales features could look like this.

What about using pipelines with cross-validation?

You can use cross_val_score with your pipeline to evaluate its performance. Just pass the pipeline and your dataset to it.

How do I handle categorical variables in a pipeline?

You can use ColumnTransformer to apply different preprocessing steps to different columns, including one-hot encoding for categorical variables.