How do I compare different pipelines in scikit-learn?

How do I compare different pipelines in scikit-learn?

You can compare different pipelines in scikit-learn using cross-validation and metrics like accuracy or F1 score.

Can you show me an example of how to set up the comparison?

Sure! You can use GridSearchCV to evaluate different pipeline configurations. Would you like a code snippet?

Yes, please provide a code snippet for that.

Here's a simple example:

from sklearn.pipeline import Pipeline
from sklearn.model_selection import GridSearchCV
from sklearn.ensemble import RandomForestClassifier

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

param_grid = {
    'classifier__n_estimators': [10, 50, 100],
    'classifier__max_depth': [None, 10, 20]
}

grid_search = GridSearchCV(pipeline, param_grid, cv=5)
grid_search.fit(X_train, y_train)

What metrics should I use to evaluate the pipelines?

Common metrics include accuracy, precision, recall, and F1 score. It depends on your specific use case!