What are the advantages of using pipelines in scikit-learn?

What are the advantages of using pipelines in scikit-learn?

Pipelines in scikit-learn streamline the workflow by combining preprocessing and modeling steps, ensuring a cleaner code.

Can you give me an example of how to create a pipeline?

Sure! You can use Pipeline from sklearn.pipeline to chain together transformers and estimators. For example:

from sklearn.pipeline import Pipeline
from sklearn.preprocessing import StandardScaler
from sklearn.linear_model import LogisticRegression

pipeline = Pipeline([
    ('scaler', StandardScaler()),
    ('classifier', LogisticRegression())
])

What are some common use cases for pipelines?

Common use cases include data preprocessing, feature selection, and model evaluation, all in a single, reproducible workflow.

How do pipelines help in avoiding data leakage?

Pipelines help prevent data leakage by ensuring that the preprocessing steps are applied only to the training data during model fitting.