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Regularization in practice: telling underfitting from overfitting and choosing the strength
A machine learning model can fail in two opposite ways.
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Rules vs model bake-off: measuring when learned models actually earn their complexity
A machine learning model achieves 77.5% accuracy. A handful of hand-written rules achieves 76.0%. The model wins the benchmark.
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Cross-validation done right: k-fold, stratification, and time-series traps
A machine learning model can achieve 97% cross-validation accuracy and still fail spectacularly when deployed. The culprit is often not the algorithm, hyperparameters, or training data. It’s the way t
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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