Automatic tuning and fitting of 'xgboost'. Use early stopping to determine the optimal number of iterations and Bayesian optimization (from 'mlrMBO') for all further parameters. Tunes class weights and thresholds in classification. Categorical features are handled efficiently either by impact encoding or dummy encoding based on the number of factor levels.
|License||BSD_2_clause + file LICENSE|
|Package repository||View on GitHub|
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