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Which model when: a practical decision playbook for tabular, text, images, and small data
Model selection gets easier when you stop asking, “What is the best machine-learning model?” and ask a more useful set of questions:
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Tuning XGBoost without fooling yourself: early stopping, learning rate, and the parameters that matter
XGBoost tuning becomes dangerous when optimization and evaluation blur together. A workflow can try dozens of configurations, stop each run at its best validation round, select the lowest validation e