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With no more context, my main guess is that it is an effectt not of the features but the metric you are using. Remember that $R^2$ is nondecreasing, so it will be greater or equal as you add more predictors I can recommend to use another metric like mean squared error and repeat the model evaluation. For reference check: https://stats.stackexchange.com/...


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Please check the size of input matrix to XGBoost, Its not possible for XGBoost to return more variables then in the input. f0 = refers to variable at 0 index Please share codes and sample data for clear drill down


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There are two important things in random forests: "bagging" and "random". Broadly speaking: bagging means that only a part of the "rows" are used at a time (see details here) while "random" means that only a small fraction of the "columns" (features, usually $\sqrt{m}$ as default) are used to make a single ...


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Allright, here's what I think: I would always recommend splitting the training set if the amount of data allows it. Your train/test split in the train data wil then be a train/validation split. The validation set will allow you to test your model for things such as overfitting on the training data. Even though you can get the ROC/AUC score for the test set ...


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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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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, ...)


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