metadata
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.
Click to expand
Hyperparameter | Value |
---|---|
memory | |
steps | [('onehotencoder', OneHotEncoder(handle_unknown='ignore', sparse=False)), ('xgbregressor', XGBRegressor(base_score=0.5, booster='gbtree', colsample_bylevel=1, colsample_bynode=1, colsample_bytree=1, enable_categorical=False, gamma=0, gpu_id=-1, importance_type=None, interaction_constraints='', learning_rate=0.300000012, max_delta_step=0, max_depth=5, min_child_weight=1, missing=nan, monotone_constraints='()', n_estimators=100, n_jobs=8, num_parallel_tree=1, predictor='auto', random_state=0, reg_alpha=0, reg_lambda=1, scale_pos_weight=1, subsample=1, tree_method='exact', 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, colsample_bynode=1, colsample_bytree=1, enable_categorical=False, gamma=0, gpu_id=-1, importance_type=None, interaction_constraints='', learning_rate=0.300000012, max_delta_step=0, max_depth=5, min_child_weight=1, missing=nan, monotone_constraints='()', n_estimators=100, n_jobs=8, num_parallel_tree=1, predictor='auto', random_state=0, reg_alpha=0, reg_lambda=1, scale_pos_weight=1, subsample=1, tree_method='exact', 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__colsample_bytree | 1 |
xgbregressor__enable_categorical | False |
xgbregressor__gamma | 0 |
xgbregressor__gpu_id | -1 |
xgbregressor__importance_type | |
xgbregressor__interaction_constraints | |
xgbregressor__learning_rate | 0.300000012 |
xgbregressor__max_delta_step | 0 |
xgbregressor__max_depth | 5 |
xgbregressor__min_child_weight | 1 |
xgbregressor__missing | nan |
xgbregressor__monotone_constraints | () |
xgbregressor__n_estimators | 100 |
xgbregressor__n_jobs | 8 |
xgbregressor__num_parallel_tree | 1 |
xgbregressor__predictor | auto |
xgbregressor__random_state | 0 |
xgbregressor__reg_alpha | 0 |
xgbregressor__reg_lambda | 1 |
xgbregressor__scale_pos_weight | 1 |
xgbregressor__subsample | 1 |
xgbregressor__tree_method | exact |
xgbregressor__validate_parameters | 1 |
xgbregressor__verbosity |
Model Plot
The model plot is below.
Pipeline(steps=[('onehotencoder',OneHotEncoder(handle_unknown='ignore', sparse=False)),('xgbregressor',XGBRegressor(base_score=0.5, booster='gbtree',colsample_bylevel=1, colsample_bynode=1,colsample_bytree=1, enable_categorical=False,gamma=0, gpu_id=-1, importance_type=None,interaction_constraints='',learning_rate=0.300000012, max_delta_step=0,max_depth=5, min_child_weight=1, missing=nan,monotone_constraints='()', n_estimators=100,n_jobs=8, num_parallel_tree=1, predictor='auto',random_state=0, reg_alpha=0, reg_lambda=1,scale_pos_weight=1, subsample=1,tree_method='exact', validate_parameters=1,verbosity=None))])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.
Pipeline(steps=[('onehotencoder',OneHotEncoder(handle_unknown='ignore', sparse=False)),('xgbregressor',XGBRegressor(base_score=0.5, booster='gbtree',colsample_bylevel=1, colsample_bynode=1,colsample_bytree=1, enable_categorical=False,gamma=0, gpu_id=-1, importance_type=None,interaction_constraints='',learning_rate=0.300000012, max_delta_step=0,max_depth=5, min_child_weight=1, missing=nan,monotone_constraints='()', n_estimators=100,n_jobs=8, num_parallel_tree=1, predictor='auto',random_state=0, reg_alpha=0, reg_lambda=1,scale_pos_weight=1, subsample=1,tree_method='exact', validate_parameters=1,verbosity=None))])
OneHotEncoder(handle_unknown='ignore', sparse=False)
XGBRegressor(base_score=0.5, booster='gbtree', colsample_bylevel=1,colsample_bynode=1, colsample_bytree=1, enable_categorical=False,gamma=0, gpu_id=-1, importance_type=None,interaction_constraints='', learning_rate=0.300000012,max_delta_step=0, max_depth=5, min_child_weight=1, missing=nan,monotone_constraints='()', n_estimators=100, n_jobs=8,num_parallel_tree=1, predictor='auto', random_state=0, reg_alpha=0,reg_lambda=1, scale_pos_weight=1, subsample=1, tree_method='exact',validate_parameters=1, verbosity=None)
Evaluation Results
[More Information Needed]
How to Get Started with the Model
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Model Card Authors
This model card is written by following authors:
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Model Card Contact
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Citation
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BibTeX:
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