What are some examples of using pipelines in scikit-learn?

What are some examples of using pipelines in scikit-learn?

Pipelines in scikit-learn streamline the process of building machine learning models. They allow you to chain together multiple steps, such as preprocessing and model training. For example, you can create a pipeline that first scales your data and then applies a classifier.

Can you show me a code example of a pipeline?

Sure! Here's a simple example:

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

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

How do I fit the pipeline to my data?

You can fit the pipeline just like a regular model. For example:

pipeline.fit(X_train, y_train)

This will scale your training data and then fit the RandomForestClassifier.

What if I want to include feature selection in the pipeline?

You can add a feature selection step to the pipeline. For instance, using SelectKBest:

from sklearn.feature_selection import SelectKBest

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