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- super object has no attribute __sklearn_tags__
I suspect it could be related to compatibility issues between Scikit-learn and XGBoost or Python version I am using Python 3 12, and both Scikit-learn and XGBoost are installed with their latest versions I attempted to tune the hyperparameters of an XGBRegressor using RandomizedSearchCV from Scikit-learn
- How to get feature importance in xgboost? - Stack Overflow
The scikit-learn like API of Xgboost is returning gain importance while get_fscore returns weight type Permutation based importance perm_importance = permutation_importance(xgb, X_test, y_test) sorted_idx = perm_importance importances_mean argsort() plt barh(boston feature_names[sorted_idx], perm_importance importances_mean[sorted_idx]) plt
- XGBoost - Poisson distribution with varying exposure offset
I am trying to use XGBoost to model claims frequency of data generated from unequal length exposure periods, but have been unable to get the model to treat the exposure correctly I would normally do
- XGBoost Categorical Variables: Dummification vs encoding
XGBoost has since version 1 3 0 added experimental support for categorical features From the docs: 1 8 7 Categorical Data Other than users performing encoding, XGBoost has experimental support for categorical data using gpu_hist and gpu_predictor No special operation needs to be done on input test data since the information about categories
- XGBOOST: sample_Weights vs scale_pos_weight - Stack Overflow
@milad-shahidi's answer covers what should happen, but empirically I've found XGBoost doesn't always conform to theory: I'd advise treating the two parameters as hyperparameters to be tuned As evidence, in the following minimal example, models trained using the model parameter class_weights and models trained using the fit parameter sample
- python - Multiclass classification with xgboost classifier? - Stack . . .
I am trying out multi-class classification with xgboost and I've built it using this code, clf = xgb XGBClassifier(max_depth=7, n_estimators=1000) clf fit(byte_train, y_train) train1 = clf predict_proba(train_data) test1 = clf predict_proba(test_data) This gave me some good results I've got log-loss below 0 7 for my case
- python - Feature importance gain in XGBoost - Stack Overflow
I wonder if xgboost also uses this approach using information gain or accuracy as stated in the citation above I've tried to dig in the code of xgboost and found out this method (already cut off irrelevant parts):
- multioutput regression by xgboost - Stack Overflow
The 2 0 0 xgboost release supports multi-target trees with vector-leaf outputs Meaning, xgboost can now build multi-output trees where the size of leaf equals the number of targets The tree method hist must be used Specify the multi_strategy = "multi_output_tree" training parameter to build a multi-output tree:
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