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Its a always a good practice to have complete unsused evaluation data set for stopping your final model. Repeating the early stopping procedure many times may result in the model overfitting the validation dataset.This can happen just as easily as overfitting the training dataset. One approach is to only use early stopping once all other hyperparameters of ...


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So if you look at how a decision trees work, it select all the variables and perform split on it to find the best split. The process of finding the best split requires iterating over each variable(assuming colsubsample & rowsubsample is 1) which is a time consuming process. In light GBM finding the best split for each variable and calculating gain from ...


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Yes, you can train XGBoost in parallel using the Dask backend. Short Solution Training XGBoost in parallel with Dask requires 2 changes in your code: substitute dtrain = xgb.DMatrix(X_train, y_train) with dtrain = xgb.dask.DaskDMatrix(X_train, y_train) substitute xgb.train(params, dtrain, ...) with xgb.dask.train(client, params, dtrain, ...) Have a look ...


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