readme: ---
  library_name: sklearn
  tags:
  - sklearn
  - skops
  - tabular-regression
  model_format: pickle
  model_file: model.pkl
  widget:
    structuredData:
      Fedu:
      - 3
      - 3
      - 3
      Fjob:
      - other
      - other
      - services
      G1:
      - 12
      - 13
      - 8
      G2:
      - 13
      - 14
      - 7
      G3:
      - 12
      - 14
      - 0
      Medu:
      - 3
      - 2
      - 1
      Mjob:
      - services
      - other
      - at_home
      Pstatus:
      - T
      - T
      - T
      Walc:
      - 2
      - 1
      - 1
      absences:
      - 2
      - 0
      - 0
      activities:
      - 'yes'
      - 'no'
      - 'yes'
      address:
      - U
      - U
      - U
      age:
      - 16
      - 16
      - 16
      failures:
      - 0
      - 0
      - 3
      famrel:
      - 4
      - 5
      - 4
      famsize:
      - GT3
      - GT3
      - GT3
      famsup:
      - 'no'
      - 'no'
      - 'no'
      freetime:
      - 2
      - 3
      - 3
      goout:
      - 3
      - 3
      - 5
      guardian:
      - mother
      - father
      - mother
      health:
      - 3
      - 3
      - 3
      higher:
      - 'yes'
      - 'yes'
      - 'yes'
      internet:
      - 'yes'
      - 'yes'
      - 'yes'
      nursery:
      - 'yes'
      - 'yes'
      - 'no'
      paid:
      - 'yes'
      - 'no'
      - 'no'
      reason:
      - home
      - home
      - home
      romantic:
      - 'yes'
      - 'no'
      - 'yes'
      school:
      - GP
      - GP
      - GP
      schoolsup:
      - 'no'
      - 'no'
      - 'no'
      sex:
      - M
      - M
      - F
      studytime:
      - 2
      - 1
      - 2
      traveltime:
      - 1
      - 2
      - 1
  ---
  
  # Model description
  
  [More Information Needed]
  
  ## Intended uses & limitations
  
  [More Information Needed]
  
  ## Training Procedure
  
  ### Hyperparameters
  
  The model is trained with below hyperparameters.
  
  <details>
  <summary> Click to expand </summary>
  
  | Hyperparameter                        | Value                                                |
  |---------------------------------------|------------------------------------------------------|
  | memory                                |                                                      |
  | steps                                 | [('onehotencoder', OneHotEncoder(handle_unknown='ignore', sparse=False)), ('xgbregressor', XGBRegressor(base_score=0.5, booster='gbtree', colsample_bylevel=1,<br />             colsample_bynode=1, colsample_bytree=1, enable_categorical=False,<br />             gamma=0, gpu_id=-1, importance_type=None,<br />             interaction_constraints='', learning_rate=0.300000012,<br />             max_delta_step=0, max_depth=5, min_child_weight=1, missing=nan,<br />             monotone_constraints='()', n_estimators=100, n_jobs=8,<br />             num_parallel_tree=1, predictor='auto', random_state=0, reg_alpha=0,<br />             reg_lambda=1, scale_pos_weight=1, subsample=1, tree_method='exact',<br />             validate_parameters=1, verbosity=None))]                                                      |
  | verbose                               | False                                                |
  | onehotencoder                         | OneHotEncoder(handle_unknown='ignore', sparse=False) |
  | xgbregressor                          | XGBRegressor(base_score=0.5, booster='gbtree', colsample_bylevel=1,<br />             colsample_bynode=1, colsample_bytree=1, enable_categorical=False,<br />             gamma=0, gpu_id=-1, importance_type=None,<br />             interaction_constraints='', learning_rate=0.300000012,<br />             max_delta_step=0, max_depth=5, min_child_weight=1, missing=nan,<br />             monotone_constraints='()', n_estimators=100, n_jobs=8,<br />             num_parallel_tree=1, predictor='auto', random_state=0, reg_alpha=0,<br />             reg_lambda=1, scale_pos_weight=1, subsample=1, tree_method='exact',<br />             validate_parameters=1, verbosity=None)                                                      |
  | onehotencoder__categories             | auto                                                 |
  | onehotencoder__drop                   |                                                      |
  | onehotencoder__dtype                  | <class 'numpy.float64'>                              |
  | onehotencoder__handle_unknown         | ignore                                               |
  | onehotencoder__max_categories         |                                                      |
  | onehotencoder__min_frequency          |                                                      |
  | onehotencoder__sparse                 | False                                                |
  | xgbregressor__objective               | reg:squarederror                                     |
  | xgbregressor__base_score              | 0.5                                                  |
  | xgbregressor__booster                 | gbtree                                               |
  | xgbregressor__colsample_bylevel       | 1                                                    |
  | xgbregressor__colsample_bynode        | 1                                                    |
  | xgbregressor__colsam