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erdogant / hgboost

hgboost is a python package for hyper-parameter optimization for xgboost, catboost or lightboost using cross-validation, and evaluating the results on an independent validation set. hgboost can be applied for classification and regression tasks.
http://erdogant.github.io/hgboost
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Specific data results problem #5

Closed recherHE closed 3 years ago

recherHE commented 3 years ago

Your package is very convenient and effective, but if I want to redraw the plot, what can I do to get specific data results? thanks for helping.

erdogant commented 3 years ago

Great! Can you elaborate a bit more about "specific"?

All the results are stored in the results dict:

hgb.results.keys()
# dict_keys(['params', 'summary', 'trials', 'model', 'val_results', 'comparison_results'])

# All the validation results can then be found with:
hgb.results['val_results'].keys()
# dict_keys(['class_names', 'pos_label', 'neg_label', 'y_true', 'y_pred', 'y_proba', 'auc', 'f1', 'kappa', 'report', 'thresholds', 'fpr', 'tpr', 'average_precision', 'precision', 'recall', 'MCC', 'CAP', 'TPFP', 'confmat', 'threshold'])
recherHE commented 3 years ago

thanks a lot! these will help me out. 

---Original--- From: "Erdogan @.> Date: Thu, Mar 18, 2021 22:48 PM To: @.>; Cc: @.**@.>; Subject: Re: [erdogant/hgboost] Specific data results problem (#5)

Great! Can you elaborate a bit more about "specific"?

All the results are stored in hgb.results.keys() # dict_keys(['params', 'summary', 'trials', 'model', 'val_results', 'comparison_results']) # All the validation results can then be found with: hgb.results['val_results'].keys() # dict_keys(['class_names', 'pos_label', 'neg_label', 'y_true', 'y_pred', 'y_proba', 'auc', 'f1', 'kappa', 'report', 'thresholds', 'fpr', 'tpr', 'average_precision', 'precision', 'recall', 'MCC', 'CAP', 'TPFP', 'confmat', 'threshold'])
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