metadata

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, ...)

In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.

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