| --- |
| 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__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 | | |
| |
| </details> |
| |
| ### Model Plot |
| |
| The model plot is below. |
| |
| <style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: "▸";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: "▾";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: "";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: "";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: "";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id="sk-container-id-1" class="sk-top-container" style="overflow: auto;"><div class="sk-text-repr-fallback"><pre>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))])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class="sk-container" hidden><div class="sk-item sk-dashed-wrapped"><div class="sk-label-container"><div class="sk-label sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-1" type="checkbox" ><label for="sk-estimator-id-1" class="sk-toggleable__label sk-toggleable__label-arrow">Pipeline</label><div class="sk-toggleable__content"><pre>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))])</pre></div></div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-2" type="checkbox" ><label for="sk-estimator-id-2" class="sk-toggleable__label sk-toggleable__label-arrow">OneHotEncoder</label><div class="sk-toggleable__content"><pre>OneHotEncoder(handle_unknown='ignore', sparse=False)</pre></div></div></div><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-3" type="checkbox" ><label for="sk-estimator-id-3" class="sk-toggleable__label sk-toggleable__label-arrow">XGBRegressor</label><div class="sk-toggleable__content"><pre>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)</pre></div></div></div></div></div></div></div> |
| |
| ## 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] |
| ``` |
| |
