That’s true that binary:logistic is the default objective for XGBClassifier, but I don’t see any reason why you couldn’t use other objectives offered by XGBoost package. For example, you can see in sklearn.py source code that multi:softprob is used explicitly in multiclass case. Moreover, if it’s really necessary, you can provide a custom objective function … Read more
One possible approach, instead of using OneVsRestClassifier which is for multi-class tasks, is to use MultiOutputClassifier from the sklearn.multioutput module. Below is a small reproducible sample code with the number of input features and target outputs requested by the OP import xgboost as xgb from sklearn.datasets import make_multilabel_classification from sklearn.model_selection import train_test_split from sklearn.multioutput import … Read more
A little bit of theory first, sorry! You asked for the grad and hessian for MAE, however, the MAE is not continuously twice differentiable so trying to calculate the first and second derivatives becomes tricky. Below we can see the “kink” at x=0 which prevents the MAE from being continuously differentiable. Moreover, the second derivative … Read more
Yes they are the same, both referring to the same parameter (see the docs here, or the github issue). The reason of the different name is because xgb.XGBRegressor is an implementation of the scikit-learn API; and scikit-learn conventionally uses n_estimators to refer to the number of boosting stages (for example the GradientBoostingClassifier)
This is the case where the order of column-names while model building is different from order of column-names while model scoring. I have used the following steps to overcome this error First load the pickle file model = pickle.load(open(“saved_model_file”, “rb”)) extraxt all the columns with order in which they were used cols_when_model_builds = model.get_booster().feature_names reorder … Read more
xgboost.train is the low-level API to train the model via gradient boosting method. xgboost.XGBRegressor and xgboost.XGBClassifier are the wrappers (Scikit-Learn-like wrappers, as they call it) that prepare the DMatrix and pass in the corresponding objective function and parameters. In the end, the fit call simply boils down to: self._Booster = train(params, dmatrix, self.n_estimators, evals=evals, early_stopping_rounds=early_stopping_rounds, … Read more
If you’re using the scikit-learn wrapper you’ll need to access the underlying XGBoost Booster and set the feature names on it, instead of the scikit model, like so: model = joblib.load(“your_saved.model”) model.get_booster().feature_names = [“your”, “feature”, “name”, “list”] xgboost.plot_importance(model.get_booster())
When using early_stopping_rounds you also have to give eval_metric and eval_set as input parameter for the fit method. Early stopping is done via calculating the error on an evaluation set. The error has to decrease every early_stopping_rounds otherwise the generation of additional trees is stopped early. See the documentation of xgboosts fit method for details. … Read more
It turns out the problem was I was using GridSearchCV from sklearn.grid_search, which is deprecated. Importing GridSearchCV from sklearn.model_selection resolved the problem: import xgboost as xgb from sklearn.model_selection import TimeSeriesSplit, GridSearchCV import numpy as np X = np.array([[4, 5, 6, 1, 0, 2], [3.1, 3.5, 1.0, 2.1, 8.3, 1.1]]).T y = np.array([1, 6, 7, 1, … Read more