README.md

metadata

license: mit
library_name: sklearn
tags:
  - sklearn
  - skops
  - tabular-regression
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

This is an XGBoost model trained to predict daily alcohol consumption of students.

Intended uses & limitations

[More Information Needed]

Training Procedure

Hyperparameters

The model is trained with below hyperparameters.

Click to expand

Hyperparameter	Value
memory
steps	[('onehotencoder', OneHotEncoder(handle_unknown='ignore', sparse=False)), ('xgbregressor', XGBRegressor(base_score=None, booster=None, callbacks=None, colsample_bylevel=None, colsample_bynode=None, colsample_bytree=None, early_stopping_rounds=None, enable_categorical=False, eval_metric=None, feature_types=None, gamma=None, gpu_id=None, grow_policy=None, importance_type=None, interaction_constraints=None, learning_rate=None, max_bin=None, max_cat_threshold=None, max_cat_to_onehot=None, max_delta_step=None, max_depth=5, max_leaves=None, min_child_weight=None, missing=nan, monotone_constraints=None, n_estimators=100, n_jobs=None, num_parallel_tree=None, predictor=None, random_state=None, ...))]
verbose	False
onehotencoder	OneHotEncoder(handle_unknown='ignore', sparse=False)
xgbregressor	XGBRegressor(base_score=None, booster=None, callbacks=None, colsample_bylevel=None, colsample_bynode=None, colsample_bytree=None, early_stopping_rounds=None, enable_categorical=False, eval_metric=None, feature_types=None, gamma=None, gpu_id=None, grow_policy=None, importance_type=None, interaction_constraints=None, learning_rate=None, max_bin=None, max_cat_threshold=None, max_cat_to_onehot=None, max_delta_step=None, max_depth=5, max_leaves=None, min_child_weight=None, missing=nan, monotone_constraints=None, n_estimators=100, n_jobs=None, num_parallel_tree=None, predictor=None, random_state=None, ...)
onehotencoder__categories	auto
onehotencoder__drop
onehotencoder__dtype	<class 'numpy.float64'>
onehotencoder__handle_unknown	ignore
onehotencoder__sparse	False
xgbregressor__objective	reg:squarederror
xgbregressor__base_score
xgbregressor__booster
xgbregressor__callbacks
xgbregressor__colsample_bylevel
xgbregressor__colsample_bynode
xgbregressor__colsample_bytree
xgbregressor__early_stopping_rounds
xgbregressor__enable_categorical	False
xgbregressor__eval_metric
xgbregressor__feature_types
xgbregressor__gamma
xgbregressor__gpu_id
xgbregressor__grow_policy
xgbregressor__importance_type
xgbregressor__interaction_constraints
xgbregressor__learning_rate
xgbregressor__max_bin
xgbregressor__max_cat_threshold
xgbregressor__max_cat_to_onehot
xgbregressor__max_delta_step
xgbregressor__max_depth	5
xgbregressor__max_leaves
xgbregressor__min_child_weight
xgbregressor__missing	nan
xgbregressor__monotone_constraints
xgbregressor__n_estimators	100
xgbregressor__n_jobs
xgbregressor__num_parallel_tree
xgbregressor__predictor
xgbregressor__random_state
xgbregressor__reg_alpha
xgbregressor__reg_lambda
xgbregressor__sampling_method
xgbregressor__scale_pos_weight
xgbregressor__subsample
xgbregressor__tree_method
xgbregressor__validate_parameters
xgbregressor__verbosity

Model Plot

The model plot is below.

Pipeline(steps=[('onehotencoder',OneHotEncoder(handle_unknown='ignore', sparse=False)),('xgbregressor',XGBRegressor(base_score=None, booster=None, callbacks=None,colsample_bylevel=None, colsample_bynode=None,colsample_bytree=None, early_stopping_rounds=None,enable_categorical=False, eval_metric=None,feature_types=None, gamma=None, gpu_id=None,grow_policy=None, importance_type=None,interaction_constraints=None, learning_rate=None,max_bin=None, max_cat_threshold=None,max_cat_to_onehot=None, max_delta_step=None,max_depth=5, max_leaves=None,min_child_weight=None, missing=nan,monotone_constraints=None, n_estimators=100,n_jobs=None, num_parallel_tree=None,predictor=None, random_state=None, ...))])

Please rerun this cell to show the HTML repr or trust the notebook.

Evaluation Results

You can find the details about evaluation process and the evaluation results.

Metric	Value
R squared	0.382
Mean Squared Error	0.43055

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]

Feature Importance Plot

Explained as: feature importances

XGBoost feature importances; values are numbers 0 <= x <= 1;all values sum to 1.

Weight	Feature
0.3592	x26_5
0.0499	x26_1
0.0383	x26_4
0.0325	x23_3
0.0256	x28_0
0.0229	x30_10
0.0222	x8_health
0.0203	x29_10
0.0200	x14_2
0.0200	x7_3
0.0199	x31_16
0.0179	x28_8
0.0155	x28_6
0.0155	x11_mother
0.0149	x29_12
0.0145	x26_2
0.0138	x21_no
0.0112	x6_2
0.0098	x14_0
0.0092	x18_no
… 161 more …