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How does gradient boosting (XGBoost/LightGBM) work, and why does it dominate tabular ML?

XGBoost and LightGBM win most tabular problems, and interviewers want more than 'it's boosting.' The signal is the fit-to-residuals mechanism plus the engineering (regularization, histograms, second-order) that makes it both fast and accurate.

Updated Aug 2026 · Grounded in real Applied AI Engineer interview loops and written to a senior-engineer editorial bar.

XGBoost and LightGBM win most tabular problems, and interviewers want more than 'it's boosting.' The signal is the fit-to-residuals mechanism plus the engineering (regularization, histograms, second-order) that makes it both fast and accurate.

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