OOF Meta-Features

Overview

Train a model to predict an auxiliary target (e.g., a related physical property, an intermediate label), collect its out-of-fold (OOF) predictions, and feed those as features into the final model. This injects learned representations without leakage — each sample's meta-feature comes from a model that never saw that sample during training.

Quick Start

import numpy as np
from sklearn.model_selection import KFold
import lightgbm as lgb

def generate_oof_feature(X, y_aux, X_test, params, n_splits=5):
    """Train on auxiliary target, return OOF predictions as features."""
    folds = KFold(n_splits=n_splits, shuffle=True, random_state=42)
    oof = np.zeros(len(X))
    test_pred = np.zeros(len(X_test))

    for train_idx, val_idx in folds.split(X):
        model = lgb.LGBMRegressor(**params)
        model.fit(X.iloc[train_idx], y_aux.iloc[train_idx],
                  eval_set=[(X.iloc[val_idx], y_aux.iloc[val_idx])],
                  callbacks=[lgb.early_stopping(100)])
        oof[val_idx] = model.predict(X.iloc[val_idx])
        test_pred += model.predict(X_test) / n_splits

    return oof, test_pred

# Usage: predict auxiliary target, add as feature
oof_aux, test_aux = generate_oof_feature(X, y_auxiliary, X_test, params)
X['meta_aux'] = oof_aux
X_test['meta_aux'] = test_aux

# Now train final model with the meta-feature included
final_model.fit(X, y_target)

Workflow

1. Identify auxiliary targets related to the main target (e.g., intermediate properties)
2. Generate OOF predictions for each auxiliary target using K-Fold
3. Average test predictions across folds (no leakage)
4. Add OOF columns to training features, averaged predictions to test features
5. Train final model with enriched feature set

Key Decisions

• No leakage: OOF ensures each sample's meta-feature comes from held-out predictions
• Same folds: Use identical fold splits for OOF generation and final training
• Multiple meta-features: Stack several auxiliary predictions — each adds a learned signal
• Diminishing returns: 2-3 meta-features usually saturate; more adds noise

References

• Predicting Molecular Properties / CHAMPS Scalar Coupling (Kaggle)
• Source: using-meta-features-to-improve-model