How can I visualize a scikit-learn pipeline?

How can I visualize a scikit-learn pipeline?

You can use the sklearn library along with matplotlib or graphviz to visualize your pipeline. Would you like a code example?

Yes, please provide a code example.

Here's a simple example:

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

pipeline = Pipeline([
    ('scaler', StandardScaler()),
    ('pca', PCA(n_components=2)),
    ('classifier', LogisticRegression())
])

# To visualize, you can use graphviz:
from sklearn import tree
import graphviz

dot_data = tree.export_graphviz(pipeline, out_file=None)
graph = graphviz.Source(dot_data)
graph.render('pipeline')

This will create a visual representation of your pipeline.

What if I want to customize the visualization?

You can customize the visualization by modifying the parameters in the export_graphviz function, such as filled, rounded, and special_characters. Would you like to know more about these options?