YAML Metadata
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Model description
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Intended uses & limitations
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Training Procedure
Hyperparameters
The model is trained with below hyperparameters.
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| Hyperparameter | Value |
|---|---|
| objective | binary:logistic |
| use_label_encoder | |
| base_score | 0.5 |
| booster | gbtree |
| callbacks | |
| colsample_bylevel | 1 |
| colsample_bynode | 1 |
| colsample_bytree | 1 |
| early_stopping_rounds | |
| enable_categorical | False |
| eval_metric | logloss |
| feature_types | |
| gamma | 3 |
| gpu_id | -1 |
| grow_policy | depthwise |
| importance_type | |
| interaction_constraints | |
| learning_rate | 0.1 |
| max_bin | 256 |
| max_cat_threshold | 64 |
| max_cat_to_onehot | 4 |
| max_delta_step | 0 |
| max_depth | 6 |
| max_leaves | 0 |
| min_child_weight | 1 |
| missing | nan |
| monotone_constraints | () |
| n_estimators | 250 |
| n_jobs | 0 |
| num_parallel_tree | 1 |
| predictor | auto |
| random_state | 1 |
| reg_alpha | 0 |
| reg_lambda | 1 |
| sampling_method | uniform |
| scale_pos_weight | 10 |
| subsample | 0.8 |
| tree_method | exact |
| validate_parameters | 1 |
| verbosity |
Model Plot
The model plot is below.
XGBClassifier(base_score=0.5, booster='gbtree', callbacks=None,colsample_bylevel=1, colsample_bynode=1, colsample_bytree=1,early_stopping_rounds=None, enable_categorical=False,eval_metric='logloss', feature_types=None, gamma=3, gpu_id=-1,grow_policy='depthwise', importance_type=None,interaction_constraints='', learning_rate=0.1, max_bin=256,max_cat_threshold=64, max_cat_to_onehot=4, max_delta_step=0,max_depth=6, max_leaves=0, min_child_weight=1, missing=nan,monotone_constraints='()', n_estimators=250, n_jobs=0,num_parallel_tree=1, predictor='auto', random_state=1, ...)In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
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XGBClassifier(base_score=0.5, booster='gbtree', callbacks=None,colsample_bylevel=1, colsample_bynode=1, colsample_bytree=1,early_stopping_rounds=None, enable_categorical=False,eval_metric='logloss', feature_types=None, gamma=3, gpu_id=-1,grow_policy='depthwise', importance_type=None,interaction_constraints='', learning_rate=0.1, max_bin=256,max_cat_threshold=64, max_cat_to_onehot=4, max_delta_step=0,max_depth=6, max_leaves=0, min_child_weight=1, missing=nan,monotone_constraints='()', n_estimators=250, n_jobs=0,num_parallel_tree=1, predictor='auto', random_state=1, ...)
Evaluation Results
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How to Get Started with the Model
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Model Card Authors
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Citation
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BibTeX:
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model_card_authors
Moro abdul Wahab
model_description
ML classification model to predict or identify failures in a generator
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