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Squeezing more out of XGBoost: feature engineering, custom objectives, and constraints
Once learning rate, depth, regularization, subsampling, and early stopping are sensible, another search rarely creates a step-change. The next gains usually come from changing one of three things:
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Categorical features and missing values in XGBoost: the native path vs one-hot
XGBoost can learn from missing numeric values and categorical columns without forcing every project through an impute-then-one-hot pipeline. The real decision is not whether native support exists; it
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Spark ML feature pipelines: VectorAssembler, StringIndexer, OneHotEncoder, and the Pipeline API
Machine learning models rarely consume raw tables directly. Before training, data usually needs cleaning, categorical encoding, feature assembly, and a consistent transformation path for future predic
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Encoders shootout: one-hot, ordinal, target, and hashing — when each wins and when it leaks
Categorical features are everywhere in tabular machine learning: