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- 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
- Cannot import xgboost in Jupyter notebook - Stack Overflow
Running a shell escape !pip3 doesn't guarantee that it will install in the kernel you are running Try: import sys print(sys base_prefix)
- 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
- How to deal with overfitting of xgboost classifier?
Please post us all your tuned xgboost's parameters; we need to see them, esp the important parameters, in particular max_depth, eta, etc And just because you found the optimal n_estimators for GS, that totally doesn't mean your model isn't overfit; those are two different things All your other parameters might well be leading to overfit
- 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):
- What are different options for objective functions available in xgboost . . .
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
- python - How is the feature score( importance) in the XGBoost package . . .
The command xgb importance returns a graph of feature importance measured by an f score What does this f score represent and how is it calculated? Output: Graph of feature importance
- python - XGBoost CV and best iteration - Stack Overflow
I am using XGBoost cv to find the optimal number of rounds for my model I would be very grateful if someone could confirm (or refute), the optimal number of rounds is: estop = 40 res = xgb cv(params, dvisibletrain, num_boost_round=1000000000, nfold=5, early_stopping_rounds=estop, seed=SEED, stratified=True) best_nrounds = res shape[0] - estop
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