Skip to content
Python Central Hub

RandomizedSearchCV for Large Parameter Spaces

Randomized search samples a fixed number of combinations from distributions.

It’s often more efficient than grid search because:

  • not all hyperparameters matter equally
  • you can explore wide ranges quickly

The idea

false

  flowchart LR
  D[Parameter distributions] --> R[Sample N combos]
  R --> CV[Cross-validation]
  CV --> B[Best found]

false

Scikit-learn example

RandomizedSearchCV
from sklearn.model_selection import RandomizedSearchCV
from sklearn.ensemble import RandomForestClassifier
 
params = {
    "n_estimators": [200, 400, 800],
    "max_depth": [None, 5, 10, 20],
    "min_samples_leaf": [1, 2, 4, 8],
}
 
search = RandomizedSearchCV(
    estimator=RandomForestClassifier(random_state=42),
    param_distributions=params,
    n_iter=20,
    cv=5,
    scoring="accuracy",
    n_jobs=-1,
    random_state=42,
)
 
search.fit(X, y)
print("best params:", search.best_params_)
print("best score:", search.best_score_)
RandomizedSearchCV
from sklearn.model_selection import RandomizedSearchCV
from sklearn.ensemble import RandomForestClassifier
 
params = {
    "n_estimators": [200, 400, 800],
    "max_depth": [None, 5, 10, 20],
    "min_samples_leaf": [1, 2, 4, 8],
}
 
search = RandomizedSearchCV(
    estimator=RandomForestClassifier(random_state=42),
    param_distributions=params,
    n_iter=20,
    cv=5,
    scoring="accuracy",
    n_jobs=-1,
    random_state=42,
)
 
search.fit(X, y)
print("best params:", search.best_params_)
print("best score:", search.best_score_)

When to use which

  • GridSearchCV: small search space
  • RandomizedSearchCV: large/continuous search space

Mini-checkpoint

If you’re tuning learning rate in [1e-4, 1], randomized search is usually better.

If this helped you, consider buying me a coffee ☕

Buy me a coffee

Was this page helpful?

Let us know how we did