How can I tune a pipeline in scikit-learn?

How can I tune a pipeline in scikit-learn?

You can tune a pipeline in scikit-learn using GridSearchCV or RandomizedSearchCV to optimize hyperparameters.

What parameters should I consider tuning?

You should consider tuning parameters like the learning rate, number of estimators, and regularization strength depending on your model.

Can you give me an example of using GridSearchCV?

Sure! You can create a pipeline and then use GridSearchCV to search over a parameter grid like this:

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

pipeline = Pipeline([...])
param_grid = {...}
search = GridSearchCV(pipeline, param_grid)
search.fit(X, y)

How do I evaluate the results of GridSearchCV?

You can evaluate the results by checking search.best_params_ for the best parameters and search.best_score_ for the best score.