How do I implement random forests in Python?

How do I implement random forests in Python?

To implement random forests in Python, you can use the RandomForestClassifier or RandomForestRegressor from the sklearn.ensemble module. Would you like a code example?

Yes, please provide a code example.

Here's a simple example:

from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import train_test_split
from sklearn.datasets import load_iris

# Load dataset
iris = load_iris()
X = iris.data
y = iris.target

# Split the dataset
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# Create a random forest classifier
clf = RandomForestClassifier(n_estimators=100)

# Train the model
clf.fit(X_train, y_train)

# Make predictions
predictions = clf.predict(X_test)
print(predictions)

What parameters can I tune in the RandomForestClassifier?

You can tune parameters like n_estimators (number of trees), max_depth (maximum depth of trees), min_samples_split, and max_features to improve model performance.