I Benchmarked 8 ML Models on CPU (No Tuning, No Tricks). Here’s What Happened

What I Did All models were tested under the same rules: Default settings from their libraries No hyperparameter tuning Same preprocessing Unique encoding for categorical features No dataset-specific tricks 3-Fold Cross Validation means CPU only Measured Single Inference P95 latency Logistic Regression and KNN were scaled for fairness. That’s it. No magic sauce. What I Measured For classification: Accuracy (CV Mean) Macro F1 (CV Mean) Single Inference P95 (ms) For regression: CV RMSE Test RMSE Single Inference P95 (ms) Because accuracy without latency is like buying a sports car without checking fuel cost. Classification Results... What Surprised Me Tree Models Still Dominate Accuracy Across datasets like: Adult Credit Default Santander Fraud Detection CatBoost, LightGBM, and XGBoost were very strong. Example: On Adult: LightGBM → 0.8734 accuracy CatBoost → 0.8726 XGBoost → 0.8594 Solid. But here’s the twist. Random Forest Is Slow. Like… Really Slow . On almost every dataset: RandomFore