How do I create a pipeline in scikit-learn?

How do I create a pipeline in scikit-learn?

To create a pipeline in scikit-learn, you can use the Pipeline class from sklearn.pipeline. It allows you to chain together multiple processing steps.

What are the steps I need to include in the pipeline?

Typically, you would include steps like data preprocessing (e.g., scaling, encoding) and the model itself. For example, you might have a pipeline with a scaler followed by a classifier.

Can you show me a code example?

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. Use the fit method with your training data:

pipeline.fit(X_train, y_train)