Random Forest

Random Forest is an ensemble learning method proposed by Leo Breiman in 2001. By combining Bootstrap sampling with random feature subsets across many decision trees, it achieves low-variance predictions — making it one of the most widely used baseline models for A-Share quantitative stock selection.

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Overview

Random Forest introduces dual randomness during tree construction:

1. Bootstrap Sampling: Each tree trains on a random sample drawn with replacement
2. Random Feature Subsets: At each split, only a random subset of features is considered (Breiman originally suggested $\sqrt{p}$ for classification or $p/3$ for regression; scikit-learn defaults to sqrt for classification and 1.0 — all features — for regression)

This design reduces inter-tree correlation, dramatically lowering variance while maintaining low bias.

Original paper: Random Forests — Leo Breiman, Machine Learning 45(1), 5–32, 2001

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Applications in A-Share Quantitative Strategies

1. Multi-Factor Stock Selection

Use value, growth, momentum, and quality factors (50+ Alpha factors) as input features with next-day return direction or future returns as labels. The model outputs scored rankings for stock selection.

2. Feature Importance — Factor Screening

feature_importances_ is computed via MDI (Mean Decrease Impurity) and provides an objective ranking of factor predictive contribution — an alternative to traditional IC-based factor ranking.

3. Out-of-Bag (OOB) Error Estimation

Setting oob_score=True provides a generalization error estimate without extra cross-validation. Note: Bootstrap resampling with replacement still breaks temporal order, so OOB cannot fully replace time-series validation in financial backtesting.

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Key Parameters (Finance-Recommended)

Parameter	Description	Recommended
n_estimators	Number of trees	100–500
max_features	Features per split	"sqrt" (classification)
max_depth	Maximum tree depth	None or 5–15
min_samples_leaf	Minimum samples per leaf	5–20
oob_score	Enable OOB evaluation	True
n_jobs	Parallel workers	-1 (all cores)
class_weight	Class imbalance handling	'balanced'

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Strengths & Limitations

Strengths:

• Robust to outliers and noise — well-suited for variable A-Share data quality
• No feature scaling required — raw factor values can be fed directly
• OOB estimate avoids data leakage, especially suitable for time series
• feature_importances_ directly produces a factor ranking

Limitations:

• Slower inference than single trees (aggregates all trees)
• High memory usage with large forests
• Generally lower accuracy than Boosting models (higher bias)

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Official References

• 📘 scikit-learn Random Forest Docs
• 📄 Original Paper: Breiman 2001
• 💻 RandomForestClassifier API
• 📊 Feature Importance Guide