feat: document models
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@@ -20,6 +20,8 @@ class XGBoostModel(TraditionalModel):
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def build_model(self) -> BaseEstimator:
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params = self.config.model_params
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# Histogram-based trees and parallelism provide fast training; default
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# logloss metric suits binary classification of gender.
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return xgb.XGBClassifier(
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n_estimators=params.get("n_estimators", 100),
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max_depth=params.get("max_depth", 6),
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@@ -28,6 +30,8 @@ class XGBoostModel(TraditionalModel):
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colsample_bytree=params.get("colsample_bytree", 0.8),
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random_state=self.config.random_seed,
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eval_metric="logloss",
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n_jobs=params.get("n_jobs", -1),
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tree_method=params.get("tree_method", "hist"),
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verbosity=2,
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)
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