---
library_name: sklearn
tags:
- sklearn
- skops
- tabular-regression
model_format: skops
model_file: model.skops
widget:
  structuredData:
    AveBedrms:
    - 0.9290780141843972
    - 0.9458483754512635
    - 1.087360594795539
    AveOccup:
    - 3.1134751773049647
    - 3.0613718411552346
    - 3.2657992565055762
    AveRooms:
    - 6.304964539007092
    - 6.945848375451264
    - 3.8884758364312266
    HouseAge:
    - 17.0
    - 15.0
    - 24.0
    Latitude:
    - 34.23
    - 36.84
    - 34.04
    Longitude:
    - -117.41
    - -119.77
    - -118.3
    MedInc:
    - 6.1426
    - 5.3886
    - 1.7109
    Population:
    - 439.0
    - 848.0
    - 1757.0
---

# Model description

[More Information Needed]

## Intended uses & limitations

[More Information Needed]

## Training Procedure

[More Information Needed]

### Hyperparameters

<details>
<summary> Click to expand </summary>

| Hyperparameter                                | Value                                                                                                                                                                                                                                                           |
|-----------------------------------------------|-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| cv                                            |                                                                                                                                                                                                                                                                 |
| estimators                                    | [('knn@5', Pipeline(steps=[('select_cols',<br />                 ColumnTransformer(transformers=[('long_and_lat', 'passthrough',<br />                                                  ['Longitude', 'Latitude'])])),<br />                ('knn', KNeighborsRegressor())]))] |
| final_estimator__alpha                        | 0.9                                                                                                                                                                                                                                                             |
| final_estimator__ccp_alpha                    | 0.0                                                                                                                                                                                                                                                             |
| final_estimator__criterion                    | friedman_mse                                                                                                                                                                                                                                                    |
| final_estimator__init                         |                                                                                                                                                                                                                                                                 |
| final_estimator__learning_rate                | 0.1                                                                                                                                                                                                                                                             |
| final_estimator__loss                         | squared_error                                                                                                                                                                                                                                                   |
| final_estimator__max_depth                    | 3                                                                                                                                                                                                                                                               |
| final_estimator__max_features                 |                                                                                                                                                                                                                                                                 |
| final_estimator__max_leaf_nodes               |                                                                                                                                                                                                                                                                 |
| final_estimator__min_impurity_decrease        | 0.0                                                                                                                                                                                                                                                             |
| final_estimator__min_samples_leaf             | 1                                                                                                                                                                                                                                                               |
| final_estimator__min_samples_split            | 2                                                                                                                                                                                                                                                               |
| final_estimator__min_weight_fraction_leaf     | 0.0                                                                                                                                                                                                                                                             |
| final_estimator__n_estimators                 | 500                                                                                                                                                                                                                                                             |
| final_estimator__n_iter_no_change             |                                                                                                                                                                                                                                                                 |
| final_estimator__random_state                 | 0                                                                                                                                                                                                                                                               |
| final_estimator__subsample                    | 1.0                                                                                                                                                                                                                                                             |
| final_estimator__tol                          | 0.0001                                                                                                                                                                                                                                                          |
| final_estimator__validation_fraction          | 0.1                                                                                                                                                                                                                                                             |
| final_estimator__verbose                      | 0                                                                                                                                                                                                                                                               |
| final_estimator__warm_start                   | False                                                                                                                                                                                                                                                           |
| final_estimator                               | GradientBoostingRegressor(n_estimators=500, random_state=0)                                                                                                                                                                                                     |
| n_jobs                                        |                                                                                                                                                                                                                                                                 |
| passthrough                                   | True                                                                                                                                                                                                                                                            |
| verbose                                       | 0                                                                                                                                                                                                                                                               |
| knn@5                                         | Pipeline(steps=[('select_cols',<br />                 ColumnTransformer(transformers=[('long_and_lat', 'passthrough',<br />                                                  ['Longitude', 'Latitude'])])),<br />                ('knn', KNeighborsRegressor())])              |
| knn@5__memory                                 |                                                                                                                                                                                                                                                                 |
| knn@5__steps                                  | [('select_cols', ColumnTransformer(transformers=[('long_and_lat', 'passthrough',<br />                                 ['Longitude', 'Latitude'])])), ('knn', KNeighborsRegressor())]                                                                                |
| knn@5__verbose                                | False                                                                                                                                                                                                                                                           |
| knn@5__select_cols                            | ColumnTransformer(transformers=[('long_and_lat', 'passthrough',<br />                                 ['Longitude', 'Latitude'])])                                                                                                                                   |
| knn@5__knn                                    | KNeighborsRegressor()                                                                                                                                                                                                                                           |
| knn@5__select_cols__n_jobs                    |                                                                                                                                                                                                                                                                 |
| knn@5__select_cols__remainder                 | drop                                                                                                                                                                                                                                                            |
| knn@5__select_cols__sparse_threshold          | 0.3                                                                                                                                                                                                                                                             |
| knn@5__select_cols__transformer_weights       |                                                                                                                                                                                                                                                                 |
| knn@5__select_cols__transformers              | [('long_and_lat', 'passthrough', ['Longitude', 'Latitude'])]                                                                                                                                                                                                    |
| knn@5__select_cols__verbose                   | False                                                                                                                                                                                                                                                           |
| knn@5__select_cols__verbose_feature_names_out | True                                                                                                                                                                                                                                                            |
| knn@5__select_cols__long_and_lat              | passthrough                                                                                                                                                                                                                                                     |
| knn@5__knn__algorithm                         | auto                                                                                                                                                                                                                                                            |
| knn@5__knn__leaf_size                         | 30                                                                                                                                                                                                                                                              |
| knn@5__knn__metric                            | minkowski                                                                                                                                                                                                                                                       |
| knn@5__knn__metric_params                     |                                                                                                                                                                                                                                                                 |
| knn@5__knn__n_jobs                            |                                                                                                                                                                                                                                                                 |
| knn@5__knn__n_neighbors                       | 5                                                                                                                                                                                                                                                               |
| knn@5__knn__p                                 | 2                                                                                                                                                                                                                                                               |
| knn@5__knn__weights                           | uniform                                                                                                                                                                                                                                                         |

</details>

### Model Plot

<style>#sk-container-id-2 {color: black;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 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-2 label.sk-toggleable__label-arrow:before {content: "▸";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: "▾";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 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-2 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-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: "";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 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-2 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-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 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-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 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-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 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-2 div.sk-text-repr-fallback {display: none;}</style><div id="sk-container-id-2" class="sk-top-container" style="overflow: auto;"><div class="sk-text-repr-fallback"><pre>StackingRegressor(estimators=[(&#x27;knn@5&#x27;,Pipeline(steps=[(&#x27;select_cols&#x27;,ColumnTransformer(transformers=[(&#x27;long_and_lat&#x27;,&#x27;passthrough&#x27;,[&#x27;Longitude&#x27;,&#x27;Latitude&#x27;])])),(&#x27;knn&#x27;,KNeighborsRegressor())]))],final_estimator=GradientBoostingRegressor(n_estimators=500,random_state=0),passthrough=True)</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-7" type="checkbox" ><label for="sk-estimator-id-7" class="sk-toggleable__label sk-toggleable__label-arrow">StackingRegressor</label><div class="sk-toggleable__content"><pre>StackingRegressor(estimators=[(&#x27;knn@5&#x27;,Pipeline(steps=[(&#x27;select_cols&#x27;,ColumnTransformer(transformers=[(&#x27;long_and_lat&#x27;,&#x27;passthrough&#x27;,[&#x27;Longitude&#x27;,&#x27;Latitude&#x27;])])),(&#x27;knn&#x27;,KNeighborsRegressor())]))],final_estimator=GradientBoostingRegressor(n_estimators=500,random_state=0),passthrough=True)</pre></div></div></div><div class="sk-serial"><div class="sk-item"><div class="sk-parallel"><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label sk-toggleable"><label>knn@5</label></div></div><div class="sk-serial"><div class="sk-item"><div class="sk-serial"><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-8" type="checkbox" ><label for="sk-estimator-id-8" class="sk-toggleable__label sk-toggleable__label-arrow">select_cols: ColumnTransformer</label><div class="sk-toggleable__content"><pre>ColumnTransformer(transformers=[(&#x27;long_and_lat&#x27;, &#x27;passthrough&#x27;,[&#x27;Longitude&#x27;, &#x27;Latitude&#x27;])])</pre></div></div></div><div class="sk-parallel"><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-9" type="checkbox" ><label for="sk-estimator-id-9" class="sk-toggleable__label sk-toggleable__label-arrow">long_and_lat</label><div class="sk-toggleable__content"><pre>[&#x27;Longitude&#x27;, &#x27;Latitude&#x27;]</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-10" type="checkbox" ><label for="sk-estimator-id-10" class="sk-toggleable__label sk-toggleable__label-arrow">passthrough</label><div class="sk-toggleable__content"><pre>passthrough</pre></div></div></div></div></div></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-11" type="checkbox" ><label for="sk-estimator-id-11" class="sk-toggleable__label sk-toggleable__label-arrow">KNeighborsRegressor</label><div class="sk-toggleable__content"><pre>KNeighborsRegressor()</pre></div></div></div></div></div></div></div></div></div></div><div class="sk-item"><div class="sk-parallel"><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label sk-toggleable"><label>final_estimator</label></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-12" type="checkbox" ><label for="sk-estimator-id-12" class="sk-toggleable__label sk-toggleable__label-arrow">GradientBoostingRegressor</label><div class="sk-toggleable__content"><pre>GradientBoostingRegressor(n_estimators=500, random_state=0)</pre></div></div></div></div></div></div></div></div></div></div></div></div>

## 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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