--- library_name: sklearn tags: - sklearn - skops - tabular-classification model_file: model.pkl widget: structuredData: x0: - -0.09914599897912607 - 0.1924502175495108 - -0.17512599701971115 x1: - -1.3527180038544737 - -0.30254418369353936 - -0.3432808784971574 x10: - -1.033043867154581 - 1.181705677961924 - -0.9707375350979036 x11: - -0.20058976250553548 - -0.4075697886243593 - 0.6689385877105022 x12: - 1.1264447260202237 - -0.3277542910601845 - -0.7061243553947382 x13: - 2.3578550805452707 - -0.2452873082756669 - 0.16449778472088433 x14: - -0.16970349843770105 - 0.15845033365719116 - 0.6702026662867782 x15: - 1.02155174900202 - -0.4957105400056871 - 0.3067590970166067 x16: - 0.6475392302343093 - -1.1039038751689478 - 0.7252174341663654 x17: - -0.6561708760276103 - 0.4740018109547748 - 0.465681500410126 x18: - -0.6490963010371028 - 0.17088731040051813 - -0.17090270391075216 x19: - 1.993556271321547 - -0.8900413826773769 - 0.4823497456924965 x2: - -0.08333069468143207 - -0.5776679970917816 - 0.4719859556084112 x20: - 0.38373337482281333 - 0.11724727885071742 - 0.41793176854856023 x21: - -0.48219399953359454 - 0.5483595851446571 - -0.2845084323579843 x22: - 0.6002099386032473 - -0.3328169335193628 - -0.1177130496330338 x23: - -0.9986427796510361 - -0.12805445675530894 - 0.16764132064699072 x24: - 0.9191079842956807 - -0.2904321748144559 - 0.9305255321835336 x25: - -1.0662088112874974 - -0.5211282845791263 - -0.4395435923307972 x26: - 0.07671124580480018 - 0.830067710593458 - 0.10148248620612801 x27: - -0.19394704099684984 - 0.3655010965468254 - 0.2082667800019003 x28: - -1.06070986806479 - 0.45059914693412395 - -0.42221731060136036 x29: - -0.49547996576705416 - 0.293080871191101 - -0.7124529042788277 x3: - 0.40319634268672655 - -0.7266844038748933 - -0.4392535240984599 x30: - 0.3177776541070613 - -0.6555347490121567 - 0.6992894776600148 x31: - 0.36132913089368796 - -0.5052005518828991 - -0.29502005278945825 x32: - -1.5275287471841141 - 0.6835310518117088 - -0.852342002620441 x33: - 0.41861643726463144 - 0.24432341138030303 - 0.28970967818031484 x34: - -0.5635425334957935 - -0.057994651130336465 - -0.5481205839673382 x35: - 0.6952237303944357 - -0.2186268698466881 - 1.083122777048039 x36: - 0.7923281272792859 - -0.27781559530809646 - 0.7338411152759391 x37: - -2.5752767636847587 - 1.386096372652616 - -0.3566644498671143 x38: - -0.24870867574122876 - 0.47352314520838223 - 0.5234704003548943 x39: - -0.1901453323468956 - -0.20338282797456578 - 0.8470486651132534 x4: - -0.5374409606451687 - -0.45754391548594736 - 0.27538071985784895 x5: - -1.7151691480844589 - 1.5828928158435347 - -0.47142929432970415 x6: - -0.5429735469430116 - 0.24865361490379212 - 0.10442729092365317 x7: - 1.5994259033001812 - -1.1704887195126548 - 0.5751493156703039 x8: - 0.5660448068869487 - -0.14629006117952106 - -0.7940429338085028 x9: - -0.14997228968462223 - 0.9027177003558653 - 0.21863455413984226 --- # Model description [More Information Needed] ## Intended uses & limitations [More Information Needed] ## Training Procedure ### Hyperparameters The model is trained with below hyperparameters.
Click to expand | 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, ...)
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## Evaluation Results [More Information Needed] # How to Get Started with the Model [More Information Needed] # Model Card Authors This model card is written by following authors: [More Information Needed] # Model Card Contact You can contact the model card authors through following channels: [More Information Needed] # Citation Below you can find information related to citation. **BibTeX:** ``` [More Information Needed] ``` # model_card_authors Moro abdul Wahab # model_description ML classification model to predict or identify failures in a generator