from sklearn.datasets import fetch_california_housing
from sklearn.ensemble import RandomForestRegressor
from sklearn.model_selection import RandomizedSearchCV
from sklearn.model_selection import train_test_split
from scipy.stats import randint

X, y = fetch_california_housing(return_X_y=True)
X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0)

# define the parameter space that will be searched over
param_distributions = {'n_estimators': randint(1, 5),
                       'max_depth': randint(5, 10)}

# now create a searchCV object and fit it to the data
search = RandomizedSearchCV(estimator=RandomForestRegressor(random_state=0),
                            n_iter=5,
                            param_distributions=param_distributions,
                            random_state=0)
search.fit(X_train, y_train)

search.best_params_

# the search object now acts like a normal random forest estimator
# with max_depth=9 and n_estimators=4
search.score(X_test, y_test)