Configure RandomForestClassifier "n_estimators" Parameter

The n_estimators parameter in scikit-learn’s RandomForestClassifier controls the number of decision trees in the ensemble.

Random Forest is an ensemble learning method that combines predictions from multiple decision trees to improve generalization performance. The n_estimators parameter determines how many trees are created.

Generally, using more trees leads to better performance, as it reduces the variance of the model without increasing the bias. However, there are diminishing returns and higher computational costs to using a very large number of trees.

The default value for n_estimators is 100.

In practice, values between 100 and 1000 are commonly used depending on the size and complexity of the dataset.

from sklearn.datasets import make_classification
from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import accuracy_score

# Generate synthetic dataset
X, y = make_classification(n_samples=1000, n_features=10, n_informative=5,
                           n_redundant=0, 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 n_estimators values
n_estimators_values = [10, 100, 500, 1000]
accuracies = []

for n in n_estimators_values:
    rf = RandomForestClassifier(n_estimators=n, random_state=42)
    rf.fit(X_train, y_train)
    y_pred = rf.predict(X_test)
    accuracy = accuracy_score(y_test, y_pred)
    accuracies.append(accuracy)
    print(f"n_estimators={n}, Accuracy: {accuracy:.3f}")

Running the example gives an output like:

n_estimators=10, Accuracy: 0.910
n_estimators=100, Accuracy: 0.920
n_estimators=500, Accuracy: 0.920
n_estimators=1000, Accuracy: 0.920

The key steps in this example are:

Generate a synthetic binary classification dataset with informative and noise features
Split the data into train and test sets
Train RandomForestClassifier models with different n_estimators values
Evaluate the accuracy of each model on the test set

Some tips and heuristics for setting n_estimators:

Start with the default value of 100 and increase it until the performance plateaus
Using more trees reduces the model’s variance, but too many can lead to overfitting
Consider the computational cost vs the benefit of using a large number of trees

Issues to consider:

The optimal number of trees depends on the size and complexity of the dataset
Using too few trees can result in underfitting, while too many can cause overfitting
There are rapidly diminishing returns in performance after a certain number of trees

See Also