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Every data science course introduces Principal Component Analysis. But most explanations stop at "how it works" - and rarely answer the more practical question: "When should you actually reach for it? When will it quietly hurt you?" This post is about that gap.
Machine learning teams love clean numbers: accuracy of 92 percent. RMSE of 3.4, F1 score of 0.88. These numbers feel objective, precise, and reassuring. They fit neatly into dashboards, slide decks, and go or no-go decisions.
This study benchmarks CatBoost, XGBoost, and TabPFN V2 across multiple Kaggle datasets, including classification, regression, and time series tasks.
