Scikit-learn Pipelines vs Regular Code

What are the main advantages of using scikit-learn pipelines over regular code?

Scikit-learn pipelines streamline the workflow by encapsulating preprocessing and modeling steps, making code cleaner and more maintainable.

Can you give me an example of how to create a pipeline in scikit-learn?

Sure! You can create a pipeline using Pipeline from sklearn.pipeline. For example:

from sklearn.pipeline import Pipeline
from sklearn.preprocessing import StandardScaler
from sklearn.ensemble import RandomForestClassifier

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

How do pipelines help in avoiding data leakage?

Pipelines help prevent data leakage by ensuring that all transformations are applied within the cross-validation process, keeping the test data separate.

Are there any downsides to using pipelines?

One downside is that pipelines can be less flexible for complex workflows, as they require a more structured approach to model building.