Hyperparameter Tuning with Search Methods
Hyperparameter tuning is crucial for optimizing candidate machine learning models and scikit-learn makes this process easier with a variety of built-in search methods. The library provides the two most used methods, GridSearchCV() and RandomizedSearchCV(), in easy to implement APIs along with their counterpart methods that implement a successive halving approach to hyperparameter search.
Scikit-learn also allows a manual approach to setting hyperparameters if you desire to adjust default values for your own training purposes: the set_params() and get_params() methods. set_params() allows users to adjust model hyperparameters programmatically, while get_params() retrieves the current hyperparameter settings. This functionality ensures flexibility when experimenting with different model configurations and can be paired with the techniques mentioned earlier for efficient tuning.
from sklearn.ensemble import RandomForestClassifier
# Create a RandomForestClassifier...
