HarnessML: Model Diversity

Use when evaluating your model ensemble — what's in it, what's missing, and whether the models are actually providing diverse perspectives on the problem.

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Why Diversity Matters

An ensemble of 6 models that all make the same predictions is effectively one model. The power of ensembling comes from combining models that make different mistakes — when one model is wrong, others compensate.

Diversity comes from three sources:

1. Different model families (tree vs linear vs neural) — different inductive biases
2. Different feature sets — models see different slices of the data
3. Different hyperparameters — models fit the data at different complexity levels

Source #1 is the strongest. Source #3 is the weakest.

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What Different Model Families Bring

Linear Models (logistic_regression, elastic_net)

Inductive bias: Monotonic relationships, additive effects.

Strengths: Stable, interpretable, calibrates well, handles high-dimensional sparse data. Often the best-calibrated model in the ensemble even when not the most discriminative.

Weaknesses: Cannot learn interactions or non-linear relationships without explicit feature engineering.

When they help the ensemble: They anchor predictions in linear signal that trees might overfit. If your linear model has a high meta-learner coefficient, it means the trees are noisy on cases where the linear signal is clear.

Gradient Boosted Trees (xgboost, lightgbm, catboost)

Inductive bias: Axis-aligned splits, interaction detection, sequential error correction.

Strengths: Handle mixed types, learn interactions natively, robust to outliers, built-in feature importance.

Why try all three:

• XGBoost: mature, well-understood regularization, good baseline
• LightGBM: faster, leaf-wise growth finds different splits than depth-wise
• CatBoost: native categorical handling, ordered boosting reduces overfitting

Two of the three often end up with >0.95 correlation and should be differentiated with different feature sets. But which two varies by dataset — try all three.

Random Forest

Inductive bias: Bagging reduces variance through decorrelated trees.

Strengths: Naturally resistant to overfitting, gives calibrated probability estimates, useful as a diversity anchor against boosted models.

When it helps the ensemble: Its errors are decorrelated from boosted trees because it doesn't sequentially correct — it averages. This is exactly the diversity ensembles need.

Neural Networks (mlp, tabnet)

Inductive bias: Learned representations, smooth decision boundaries.

Strengths: Can learn complex non-linear relationships, representation learning, flexible architecture.

Weaknesses: Need more data, harder to regularize, less interpretable, training sensitive to hyperparameters.

When they help the ensemble: On datasets with enough rows (10k+), neural networks learn different representations than trees. If your neural net has low correlation with tree models, it's capturing genuinely different signal.

Other Models (svm, histgbm, gam, ngboost)

SVM: Different decision boundary geometry. Useful when classes are separable in high-dimensional space.

HistGBM: sklearn's histogram-based gradient boosting. Similar to LightGBM but integrates with sklearn pipeline. Good for when you want a second tree-based model with different implementation details.

GAM (Generalized Additive Model): Learns smooth non-linear relationships for each feature independently. Highly interpretable. Useful as a "step up from linear" that doesn't overfit like trees can.

NGBoost: Probabilistic predictions with uncertainty. If your use case values uncertainty quantification, NGBoost provides it natively.

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Evaluating Your Ensemble

Check Prediction Correlations

features(action="diversity")

• >0.95 correlation between two models: They're making nearly identical predictions. One is redundant. Differentiate them (different features, different hyperparameters) or drop one.
• 0.7-0.9 correlation: Healthy. Models agree on easy cases, disagree on hard cases.
• <0.7 correlation: Very diverse. Make sure both models are individually competent — low correlation from a bad model just adds noise.

Check Meta-Learner Coefficients

pipeline(action="diagnostics")

• High positive coefficient: Model contributes strong unique signal.
• Near-zero coefficient: Model is redundant with others. The ensemble ignores it.
• Negative coefficient: Model actively hurts when combined. Its signal is anti-correlated with truth given the other models. Consider removing.

Check LOMO (Leave-One-Model-Out) Impact

Remove each model from the ensemble one at a time and measure the impact. Models whose removal hurts performance are carrying their weight. Models whose removal is neutral or positive should be removed or replaced.

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Building Diversity

Give Each Model Type a Fair Trial

Don't dismiss a model family after one configuration. A logistic regression with the wrong features looks terrible. A logistic regression with well-engineered features can be the best-calibrated model in the ensemble.

Minimum fair trial:

• 2-3 configurations with different feature sets or key structural parameters
• Evaluate both individual performance AND contribution to ensemble diversity
• A model with mediocre individual performance but low correlation with others may still improve the ensemble

Differentiate with Features

The most powerful diversification lever. Give different models different feature sets:

models(action="update", name="lr_main", features=[...])  # linear-friendly
models(action="update", name="xgb_main", features=[...])  # all features
models(action="update", name="mlp_main", features=[...])  # normalized numerics

This creates genuine diversity because models literally see different data.

When to Add vs Replace

• Add when prediction correlation with existing models is <0.9 and individual performance is reasonable
• Replace when a new model is better than an existing one with similar correlation profile
• Remove when meta-learner coefficient is near-zero or negative and LOMO shows no loss

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Common Traps

• Model count as a goal: 8 models with 0.98 pairwise correlation is worse than 4 diverse models. Measure diversity, not count.
• Only trying gradient boosted trees: XGBoost + LightGBM + CatBoost is three implementations of the same idea. You need different families.
• Dismissing linear models: "Logistic regression is too simple." It's not — it's your calibration anchor and the first model to tell you if your features carry signal.
• Dismissing neural nets on small data: MLP with dropout and batch norm can work on 5k rows. Try it before assuming.
• Uniform feature sets: Every model seeing every feature is a missed diversity opportunity.