Configure RandomForestRegressor "verbose" Parameter

from sklearn.datasets import make_regression
from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestRegressor
from sklearn.metrics import mean_squared_error

# Generate synthetic regression dataset
X, y = make_regression(n_samples=1000, n_features=10, noise=0.1, random_state=42)

# Split into train and test sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# Train with different verbose values
verbose_values = [0, 1, 2]

for v in verbose_values:
    rf = RandomForestRegressor(n_estimators=100, verbose=v, random_state=42)
    rf.fit(X_train, y_train)
    y_pred = rf.predict(X_test)
    mse = mean_squared_error(y_test, y_pred)
    print(f"\nverbose={v}, MSE: {mse:.3f}")

verbose=0, MSE: 2621.793
[Parallel(n_jobs=1)]: Done  49 tasks      | elapsed:    0.2s
[Parallel(n_jobs=1)]: Done  49 tasks      | elapsed:    0.0s

verbose=1, MSE: 2621.793
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[Parallel(n_jobs=1)]: Done  40 tasks      | elapsed:    0.2s
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[Parallel(n_jobs=1)]: Done  40 tasks      | elapsed:    0.0s

verbose=2, MSE: 2621.793