Most people think hyperparameter tuning is a magic speed boost for ML. They're overthinking it. Here's what actually works ↓ I recently worked with a real student dataset and tested this. Four different classifiers. All carefully tuned. Performance barely moved. Why? Because the defaults were already strong. The dataset was small. The signal was weak. And we used rigorous nested cross‑validation with statistical tests. No hype. No leakage. Just honest validation. In that setup, tuning didn’t sav
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