Classification Benchmarks
All models are evaluated under default settings from their respective libraries.
- No hyperparameter tuning.
- Logistic Regression and KNN is scaled for transparency.
- Same preprocessing for all models:
- Unique encoding for categorical features
- No dataset-specific tricks
- Metrics reported are 3-Fold Cross Validation means.
- Latency is measured on CPU.
Metrics shown:
- Accuracy (CV Mean)
- Macro F1 (CV Mean)
- Single Inference P95 (ms)
| Model |
Accuracy |
Macro F1 |
Single P95 (ms) |
| LightGBM |
0.8734 |
0.8170 |
1.37 |
| XGBoost |
0.8594 |
0.7812 |
0.59 |
| CatBoost |
0.8726 |
0.8147 |
0.79 |
| RandomForest |
0.8638 |
0.7994 |
24.52 |
| LogReg |
0.8491 |
0.7765 |
0.20 |
| KNN |
0.8377 |
0.7671 |
2.14 |
| SmartKNN |
0.8375 |
0.7680 |
0.21 |
| DecisionTree |
0.8051 |
0.7399 |
0.38 |
| Model |
Accuracy |
Macro F1 |
Single P95 (ms) |
| CatBoost |
0.8194 |
0.6842 |
1.09 |
| LightGBM |
0.8201 |
0.6835 |
1.57 |
| XGBoost |
0.8128 |
0.6763 |
0.63 |
| RandomForest |
0.8171 |
0.6811 |
25.47 |
| LogReg |
0.8104 |
0.6259 |
0.24 |
| KNN |
0.7939 |
0.6503 |
2.39 |
| SmartKNN |
0.7933 |
0.6554 |
0.23 |
| DecisionTree |
0.7312 |
0.6188 |
0.18 |
| Model |
Accuracy |
Macro F1 |
Single P95 (ms) |
| XGBoost |
0.9633 |
0.4921 |
0.60 |
| CatBoost |
0.9635 |
0.4911 |
0.82 |
| LightGBM |
0.9636 |
0.4909 |
1.35 |
| RandomForest |
0.9635 |
0.4909 |
23.90 |
| LogReg |
0.9635 |
0.4911 |
0.21 |
| DecisionTree |
0.9158 |
0.5058 |
0.18 |
| KNN |
0.9630 |
0.4920 |
34.67 |
| SmartKNN |
0.9629 |
0.4928 |
0.35 |
| Model |
Accuracy |
Macro F1 |
Single P95 (ms) |
| SmartKNN |
0.9982 |
0.9982 |
0.36 |
| KNN |
0.9982 |
0.9982 |
1.46 |
| CatBoost |
0.9973 |
0.9972 |
0.79 |
| LightGBM |
0.9918 |
0.9917 |
1.20 |
| XGBoost |
0.9918 |
0.9917 |
0.56 |
| RandomForest |
0.9882 |
0.9880 |
24.70 |
| DecisionTree |
0.9836 |
0.9834 |
0.18 |
| LogReg |
0.9836 |
0.9834 |
0.32 |
| Model |
Accuracy |
Macro F1 |
Single P95 (ms) |
| CatBoost |
0.9220 |
0.6994 |
1.17 |
| LogReg |
0.9144 |
0.6722 |
0.21 |
| XGBoost |
0.9120 |
0.6520 |
0.60 |
| LightGBM |
0.9081 |
0.5661 |
1.76 |
| SmartKNN |
0.8994 |
0.4755 |
0.56 |
| KNN |
0.8994 |
0.4745 |
35.61 |
| RandomForest |
0.8995 |
0.4735 |
36.09 |
| DecisionTree |
0.8336 |
0.5535 |
0.18 |
| Model |
Accuracy |
Macro F1 |
Single P95 (ms) |
| CatBoost |
0.9996 |
0.9313 |
0.84 |
| RandomForest |
0.9995 |
0.9226 |
25.01 |
| SmartKNN |
0.9995 |
0.9214 |
0.31 |
| XGBoost |
0.9995 |
0.9128 |
0.63 |
| KNN |
0.9994 |
0.9049 |
12.51 |
| DecisionTree |
0.9991 |
0.8710 |
0.16 |
| LogReg |
0.9992 |
0.8650 |
0.23 |
| LightGBM |
0.9961 |
0.6495 |
1.32 |
| Model |
Accuracy |
Macro F1 |
Single P95 (ms) |
| RandomForest |
0.9423 |
0.9423 |
25.06 |
| SmartKNN |
0.9352 |
0.9352 |
0.48 |
| DecisionTree |
0.9325 |
0.9325 |
0.16 |
| KNN |
0.9164 |
0.9164 |
22.90 |
| CatBoost |
0.9107 |
0.9107 |
0.96 |
| LightGBM |
0.8486 |
0.8486 |
1.44 |
| LogReg |
0.7706 |
0.7703 |
0.22 |
| XGBoost |
0.6901 |
0.6601 |
0.65 |