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README.md

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+---
+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.
+
+<details>
+<summary> Click to expand </summary>
+
+| Hyperparameter                        | Value                                                |
+|---------------------------------------|------------------------------------------------------|
+| memory                                |                                                      |
+| steps                                 | [('onehotencoder', OneHotEncoder(handle_unknown='ignore', sparse=False)), ('xgbregressor', XGBRegressor(base_score=None, booster=None, callbacks=None,<br />             colsample_bylevel=None, colsample_bynode=None,<br />             colsample_bytree=None, early_stopping_rounds=None,<br />             enable_categorical=False, eval_metric=None, feature_types=None,<br />             gamma=None, gpu_id=None, grow_policy=None, importance_type=None,<br />             interaction_constraints=None, learning_rate=None, max_bin=None,<br />             max_cat_threshold=None, max_cat_to_onehot=None,<br />             max_delta_step=None, max_depth=5, max_leaves=None,<br />             min_child_weight=None, missing=nan, monotone_constraints=None,<br />             n_estimators=100, n_jobs=None, num_parallel_tree=None,<br />             predictor=None, random_state=None, ...))]                                                      |
+| verbose                               | False                                                |
+| onehotencoder                         | OneHotEncoder(handle_unknown='ignore', sparse=False) |
+| xgbregressor                          | XGBRegressor(base_score=None, booster=None, callbacks=None,<br />             colsample_bylevel=None, colsample_bynode=None,<br />             colsample_bytree=None, early_stopping_rounds=None,<br />             enable_categorical=False, eval_metric=None, feature_types=None,<br />             gamma=None, gpu_id=None, grow_policy=None, importance_type=None,<br />             interaction_constraints=None, learning_rate=None, max_bin=None,<br />             max_cat_threshold=None, max_cat_to_onehot=None,<br />             max_delta_step=None, max_depth=5, max_leaves=None,<br />             min_child_weight=None, missing=nan, monotone_constraints=None,<br />             n_estimators=100, n_jobs=None, num_parallel_tree=None,<br />             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               |                                                      |
+
+</details>
+
+### Model Plot
+
+The model plot is below.
+
+<style>#sk-d0e2e311-416b-4a48-aa9a-44adf04b1ee3 {color: black;background-color: white;}#sk-d0e2e311-416b-4a48-aa9a-44adf04b1ee3 pre{padding: 0;}#sk-d0e2e311-416b-4a48-aa9a-44adf04b1ee3 div.sk-toggleable {background-color: white;}#sk-d0e2e311-416b-4a48-aa9a-44adf04b1ee3 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-d0e2e311-416b-4a48-aa9a-44adf04b1ee3 label.sk-toggleable__label-arrow:before {content: "▸";float: left;margin-right: 0.25em;color: #696969;}#sk-d0e2e311-416b-4a48-aa9a-44adf04b1ee3 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-d0e2e311-416b-4a48-aa9a-44adf04b1ee3 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-d0e2e311-416b-4a48-aa9a-44adf04b1ee3 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-d0e2e311-416b-4a48-aa9a-44adf04b1ee3 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-d0e2e311-416b-4a48-aa9a-44adf04b1ee3 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-d0e2e311-416b-4a48-aa9a-44adf04b1ee3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: "▾";}#sk-d0e2e311-416b-4a48-aa9a-44adf04b1ee3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-d0e2e311-416b-4a48-aa9a-44adf04b1ee3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-d0e2e311-416b-4a48-aa9a-44adf04b1ee3 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-d0e2e311-416b-4a48-aa9a-44adf04b1ee3 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-d0e2e311-416b-4a48-aa9a-44adf04b1ee3 div.sk-estimator:hover {background-color: #d4ebff;}#sk-d0e2e311-416b-4a48-aa9a-44adf04b1ee3 div.sk-parallel-item::after {content: "";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-d0e2e311-416b-4a48-aa9a-44adf04b1ee3 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-d0e2e311-416b-4a48-aa9a-44adf04b1ee3 div.sk-serial::before {content: "";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 2em;bottom: 0;left: 50%;}#sk-d0e2e311-416b-4a48-aa9a-44adf04b1ee3 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;}#sk-d0e2e311-416b-4a48-aa9a-44adf04b1ee3 div.sk-item {z-index: 1;}#sk-d0e2e311-416b-4a48-aa9a-44adf04b1ee3 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;}#sk-d0e2e311-416b-4a48-aa9a-44adf04b1ee3 div.sk-parallel::before {content: "";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 2em;bottom: 0;left: 50%;}#sk-d0e2e311-416b-4a48-aa9a-44adf04b1ee3 div.sk-parallel-item {display: flex;flex-direction: column;position: relative;background-color: white;}#sk-d0e2e311-416b-4a48-aa9a-44adf04b1ee3 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-d0e2e311-416b-4a48-aa9a-44adf04b1ee3 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-d0e2e311-416b-4a48-aa9a-44adf04b1ee3 div.sk-parallel-item:only-child::after {width: 0;}#sk-d0e2e311-416b-4a48-aa9a-44adf04b1ee3 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;position: relative;}#sk-d0e2e311-416b-4a48-aa9a-44adf04b1ee3 div.sk-label label {font-family: monospace;font-weight: bold;background-color: white;display: inline-block;line-height: 1.2em;}#sk-d0e2e311-416b-4a48-aa9a-44adf04b1ee3 div.sk-label-container {position: relative;z-index: 2;text-align: center;}#sk-d0e2e311-416b-4a48-aa9a-44adf04b1ee3 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-d0e2e311-416b-4a48-aa9a-44adf04b1ee3 div.sk-text-repr-fallback {display: none;}</style><div id="sk-d0e2e311-416b-4a48-aa9a-44adf04b1ee3" class="sk-top-container" style="overflow: auto;"><div class="sk-text-repr-fallback"><pre>Pipeline(steps=[(&#x27;onehotencoder&#x27;,OneHotEncoder(handle_unknown=&#x27;ignore&#x27;, sparse=False)),(&#x27;xgbregressor&#x27;,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, ...))])</pre><b>Please rerun this cell to show the HTML repr or trust the notebook.</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="3e1fc9fd-9464-4cf2-a34f-716e1f03bb90" type="checkbox" ><label for="3e1fc9fd-9464-4cf2-a34f-716e1f03bb90" class="sk-toggleable__label sk-toggleable__label-arrow">Pipeline</label><div class="sk-toggleable__content"><pre>Pipeline(steps=[(&#x27;onehotencoder&#x27;,OneHotEncoder(handle_unknown=&#x27;ignore&#x27;, sparse=False)),(&#x27;xgbregressor&#x27;,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, ...))])</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="064b4f21-1fc7-4646-9751-108c0cbbd266" type="checkbox" ><label for="064b4f21-1fc7-4646-9751-108c0cbbd266" class="sk-toggleable__label sk-toggleable__label-arrow">OneHotEncoder</label><div class="sk-toggleable__content"><pre>OneHotEncoder(handle_unknown=&#x27;ignore&#x27;, 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="8239516d-467c-4346-82ae-95b2c33e2b8a" type="checkbox" ><label for="8239516d-467c-4346-82ae-95b2c33e2b8a" class="sk-toggleable__label sk-toggleable__label-arrow">XGBRegressor</label><div class="sk-toggleable__content"><pre>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, ...)</pre></div></div></div></div></div></div></div>
+
+## 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
+
+<style>table.eli5-weights tr:hover {filter: brightness(85%);}</style><p>Explained as: feature importances</p><pre>XGBoost feature importances; values are numbers 0 <= x <= 1;all values sum to 1.</pre><table class="eli5-weights eli5-feature-importances" style="border-collapse: collapse; border: none; margin-top: 0em; table-layout: auto;"><thead><tr style="border: none;"><th style="padding: 0 1em 0 0.5em; text-align: right; border: none;">Weight</th><th style="padding: 0 0.5em 0 0.5em; text-align: left; border: none;">Feature</th></tr></thead><tbody><tr style="background-color: hsl(120, 100.00%, 80.00%); border: none;"><td style="padding: 0 1em 0 0.5em; text-align: right; border: none;">0.3592</td><td style="padding: 0 0.5em 0 0.5em; text-align: left; border: none;">x26_5</td></tr><tr style="background-color: hsl(120, 100.00%, 94.98%); border: none;"><td style="padding: 0 1em 0 0.5em; text-align: right; border: none;">0.0499</td><td style="padding: 0 0.5em 0 0.5em; text-align: left; border: none;">x26_1</td></tr><tr style="background-color: hsl(120, 100.00%, 95.83%); border: none;"><td style="padding: 0 1em 0 0.5em; text-align: right; border: none;">0.0383</td><td style="padding: 0 0.5em 0 0.5em; text-align: left; border: none;">x26_4</td></tr><tr style="background-color: hsl(120, 100.00%, 96.28%); border: none;"><td style="padding: 0 1em 0 0.5em; text-align: right; border: none;">0.0325</td><td style="padding: 0 0.5em 0 0.5em; text-align: left; border: none;">x23_3</td></tr><tr style="background-color: hsl(120, 100.00%, 96.85%); border: none;"><td style="padding: 0 1em 0 0.5em; text-align: right; border: none;">0.0256</td><td style="padding: 0 0.5em 0 0.5em; text-align: left; border: none;">x28_0</td></tr><tr style="background-color: hsl(120, 100.00%, 97.09%); border: none;"><td style="padding: 0 1em 0 0.5em; text-align: right; border: none;">0.0229</td><td style="padding: 0 0.5em 0 0.5em; text-align: left; border: none;">x30_10</td></tr><tr style="background-color: hsl(120, 100.00%, 97.15%); border: none;"><td style="padding: 0 1em 0 0.5em; text-align: right; border: none;">0.0222</td><td style="padding: 0 0.5em 0 0.5em; text-align: left; border: none;">x8_health</td></tr><tr style="background-color: hsl(120, 100.00%, 97.32%); border: none;"><td style="padding: 0 1em 0 0.5em; text-align: right; border: none;">0.0203</td><td style="padding: 0 0.5em 0 0.5em; text-align: left; border: none;">x29_10</td></tr><tr style="background-color: hsl(120, 100.00%, 97.35%); border: none;"><td style="padding: 0 1em 0 0.5em; text-align: right; border: none;">0.0200</td><td style="padding: 0 0.5em 0 0.5em; text-align: left; border: none;">x14_2</td></tr><tr style="background-color: hsl(120, 100.00%, 97.35%); border: none;"><td style="padding: 0 1em 0 0.5em; text-align: right; border: none;">0.0200</td><td style="padding: 0 0.5em 0 0.5em; text-align: left; border: none;">x7_3</td></tr><tr style="background-color: hsl(120, 100.00%, 97.36%); border: none;"><td style="padding: 0 1em 0 0.5em; text-align: right; border: none;">0.0199</td><td style="padding: 0 0.5em 0 0.5em; text-align: left; border: none;">x31_16</td></tr><tr style="background-color: hsl(120, 100.00%, 97.55%); border: none;"><td style="padding: 0 1em 0 0.5em; text-align: right; border: none;">0.0179</td><td style="padding: 0 0.5em 0 0.5em; text-align: left; border: none;">x28_8</td></tr><tr style="background-color: hsl(120, 100.00%, 97.78%); border: none;"><td style="padding: 0 1em 0 0.5em; text-align: right; border: none;">0.0155</td><td style="padding: 0 0.5em 0 0.5em; text-align: left; border: none;">x28_6</td></tr><tr style="background-color: hsl(120, 100.00%, 97.78%); border: none;"><td style="padding: 0 1em 0 0.5em; text-align: right; border: none;">0.0155</td><td style="padding: 0 0.5em 0 0.5em; text-align: left; border: none;">x11_mother</td></tr><tr style="background-color: hsl(120, 100.00%, 97.85%); border: none;"><td style="padding: 0 1em 0 0.5em; text-align: right; border: none;">0.0149</td><td style="padding: 0 0.5em 0 0.5em; text-align: left; border: none;">x29_12</td></tr><tr style="background-color: hsl(120, 100.00%, 97.89%); border: none;"><td style="padding: 0 1em 0 0.5em; text-align: right; border: none;">0.0145</td><td style="padding: 0 0.5em 0 0.5em; text-align: left; border: none;">x26_2</td></tr><tr style="background-color: hsl(120, 100.00%, 97.96%); border: none;"><td style="padding: 0 1em 0 0.5em; text-align: right; border: none;">0.0138</td><td style="padding: 0 0.5em 0 0.5em; text-align: left; border: none;">x21_no</td></tr><tr style="background-color: hsl(120, 100.00%, 98.24%); border: none;"><td style="padding: 0 1em 0 0.5em; text-align: right; border: none;">0.0112</td><td style="padding: 0 0.5em 0 0.5em; text-align: left; border: none;">x6_2</td></tr><tr style="background-color: hsl(120, 100.00%, 98.39%); border: none;"><td style="padding: 0 1em 0 0.5em; text-align: right; border: none;">0.0098</td><td style="padding: 0 0.5em 0 0.5em; text-align: left; border: none;">x14_0</td></tr><tr style="background-color: hsl(120, 100.00%, 98.47%); border: none;"><td style="padding: 0 1em 0 0.5em; text-align: right; border: none;">0.0092</td><td style="padding: 0 0.5em 0 0.5em; text-align: left; border: none;">x18_no</td></tr><tr style="background-color: hsl(120, 100.00%, 98.47%); border: none;"><td colspan="2" style="padding: 0 0.5em 0 0.5em; text-align: center; border: none; white-space: nowrap;"><i>&hellip; 161 more &hellip;</i></td></tr></tbody></table>

config.json

@@ -0,0 +1,208 @@
+{
+    "sklearn": {
+        "columns": [
+            "school",
+            "sex",
+            "age",
+            "address",
+            "famsize",
+            "Pstatus",
+            "Medu",
+            "Fedu",
+            "Mjob",
+            "Fjob",
+            "reason",
+            "guardian",
+            "traveltime",
+            "studytime",
+            "failures",
+            "schoolsup",
+            "famsup",
+            "paid",
+            "activities",
+            "nursery",
+            "higher",
+            "internet",
+            "romantic",
+            "famrel",
+            "freetime",
+            "goout",
+            "Walc",
+            "health",
+            "absences",
+            "G1",
+            "G2",
+            "G3"
+        ],
+        "environment": [
+            "scikit-learn=1.0.2, xgboost=1.7.3"
+        ],
+        "example_input": {
+            "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": {
+            "file": "model.pkl"
+        },
+        "model_format": "pickle",
+        "task": "tabular-regression"
+    }
+}

model.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:f3a554fc0488edc63273dd53fdb1508ff0f4782b8973436fa20f12bf80b4da2a
+size 186871