Machine Learning - Random Forest

This module provides a basic implementation of a Random Forest Predictor for quantitative finance. It uses scikit-learn's RandomForestRegressor to predict time series data or returns based on a set of features.

Concepts

• Ensemble Learning: Random Forest is an ensemble method that combines multiple decision trees to improve predictive performance and control over-fitting.
• Feature Importance: Trees can naturally provide a ranking of feature importance, which is very useful in quant research.

Example

Run python rf_predictor.py to see a quick demonstration of training and evaluating the model on synthetic data.