binary-classification
Binary Classification with XGBoost (Done Right)
For tabular binary classification, default to XGBoost. It dominates Kaggle and real-world benchmarks for tabular data, handles missing values and mixed feature types essentially for free, and gives you SHAP-based explanations as a side effect. This skill covers the four things that separate "ROC-AUC on a notebook" from "a model you can deploy and trust."
When to use this skill
- The target is binary (0/1, yes/no, churned/retained, fraud/legit)
- The features are tabular (numbers, categories, dates) — not images, text, or audio
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