How can I implement a Random Forest model in Python?

How can I implement a Random Forest model in Python?

You can use the RandomForestClassifier from the sklearn.ensemble module. First, install scikit-learn if you haven't already.

What are the steps to prepare my data for this model?

You'll need to clean your data, handle missing values, and split it into features and labels. Then, you can use train_test_split to create training and testing datasets.

How do I evaluate the model's performance?

You can use metrics like accuracy, precision, recall, and the confusion matrix. The classification_report from sklearn.metrics is also helpful.

Can you show me a simple code example?

Sure! Here's a basic example:

from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import classification_report

# Load your data
X, y = load_data()  # Replace with your data loading method
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)

# Create and train the model
model = RandomForestClassifier()
model.fit(X_train, y_train)

# Make predictions
predictions = model.predict(X_test)

# Evaluate the model
print(classification_report(y_test, predictions))