|
--- |
|
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. |
|
|
|
|
|
## 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=[('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, ...))])</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=[('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, ...))])</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='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="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 | |
|
|
|
|
|
# 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>… 161 more …</i></td></tr></tbody></table> |
|
|
