AdaptiveBayes vs LogisticRegression: Benchmark Analysis

Overview

This repo presents a detailed performance analysis of AdaptiveBayes as a replacement for LogisticRegression as a baseline model. Results are based on classic ML datasets and a wide range of large-scale benchmarks.

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Datasets Used

Classic Benchmarks:

• breast_cancer — breast cancer classification
• diabetes — diabetes prediction
• iris — iris species classification
• wine — wine variety classification
• heart_disease — heart disease diagnosis

Large-scale ML Benchmarks:

• CreditCardFraud — fraud detection
• HIGGS — particle classification (high-energy physics)
• SUSY — physics benchmark for supersymmetric particles
• KDDCup99 — network intrusion detection
• Covertype — forest cover prediction by geodata
• Hepmass (synthetic) — particle classification (synthetic)
• Avazu — click-through rate prediction

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Key Advantages of AdaptiveBayes

• Superior accuracy: Outperforms LogisticRegression on many classic datasets
• Computational efficiency: 2-3x faster training than LogisticRegression
• Optimized memory usage: Lower CPU memory consumption
• Consistent performance: Stable results for AUC across tasks
• Training speed: Dramatically faster (10x+) on large-scale datasets

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Critical Metrics

Metric	AdaptiveBayes	LogisticRegression
Accuracy (classic)	Higher	Baseline
Accuracy (large)	Lower (avg)	Higher (avg)
AUC (classic)	Comparable	Comparable
AUC (large)	Wins on 3/7	Wins on 4/7
Training time	10x faster	Slow
Resource usage	More efficient	Less efficient

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Summary Tables

Large Benchmark: Accuracy and AUC

Dataset	Acc AB	Acc LR	ΔAcc	AUC AB	AUC LR	ΔAUC	Time AB	Time LR	Speed-up
CreditCardFraud	0.036	0.998	-0.962	0.913	0.629	+0.28	0.1s	32.2s	322x
HIGGS	0.581	0.642	-0.061	0.589	0.685	-0.10	8.1s	18.1s	2.2x
SUSY	0.654	0.789	-0.135	0.700	0.858	-0.16	1.0s	39.4s	39.4x
KDDCup99	0.936	0.889	+0.047	0.988	0.861	+0.13	1.0s	69.9s	70x
Covertype	0.410	0.692	-0.282	-	-	-	0.3s	224.7s	749x
Synthetic	0.573	0.672	-0.099	0.688	0.720	-0.03	0.2s	4.0s	20x
Avazu	0.523	0.840	-0.317	0.664	0.502	+0.16	1.0s	7.7s	7.7x

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Graphs

1. AdaptiveBayes vs LogisticRegression Performance Comparison

2. Model Performance: Accuracy vs Training Time

Label shapes by dataset:

• ○ Circle — CreditCardFraud
• □ Square — HIGGS
• △ Triangle — SUSY
• ◇ Diamond — KDDCup99
• ★ Star — Covertype
• • Plus — Synthetic
• ✕ Cross — Avazu

Colors by model:

• Red: AdaptiveBayes
• Green: XGBoost
• Blue: RandomForest
• Orange: MLP
• Purple: LightGBM
• Brown: LogisticRegression

3. Comprehensive Performance Comparison Across 12 Datasets

4. Model Efficiency: Performance-to-Time Ratio

5. Memory Usage Comparison Across Models

6. Training vs Prediction Time Analysis

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Observations

• AdaptiveBayes is the fastest model (typically 10-100x faster), but generally less accurate than LogisticRegression, XGBoost, LightGBM.
• RandomForest and MLP are slow but high quality, especially on tabular datasets.
• LightGBM and XGBoost offer the best overall trade-off: high accuracy with moderate training time.
• AdaptiveBayes is the only model to leverage GPU memory out of the box.

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Model Selection Guide

• For production with memory constraints: Use XGBoost.
• For maximum training speed: AdaptiveBayes.
• For maximum accuracy: RandomForest (if memory is not critical).
• For balance: LightGBM or XGBoost.

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Technical Analysis and Suggestions

Current AdaptiveBayes Features:

• log1p-based transformation, adaptive learning rate, GPU support (CuPy)
• Selective feature updates for sparse data

Current Accuracy Issues:

• log1p transform can distort features for highly skewed data
• Naive adaptation of learning rate ignores curvature of loss
• Missing regularization increases overfitting risk
• Simple weight initialization may be suboptimal

Enhancement Proposals:

1. Improved feature transforms (tanh, arctan, identity)
2. L1+L2 regularization
3. Adaptive LR with uncertainty/confidence
4. Newton's step using Hessian diagonal approximation
5. Xavier or He initialization
6. Momentum & Early Stopping
7. Explicit bias term

Expected accuracy boost: 2-8% depending on dataset.

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Conclusion

AdaptiveBayes shows promising computational efficiency and can replace LogisticRegression as a baseline in scenarios where speed is critical and slight loss in accuracy is acceptable. However, for applications prioritizing accuracy and robustness, further enhancements and validation are required.

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Authors

• @sergiikavun

References

For citing you should use:

Sergii Kavun. (2025). s-kav/adaptive_bayes: version 1.0. AdaptiveBayes vs LogisticRegression: Benchmark Analysis. Zenodo. https://doi.org/10.5281/zenodo.17184114