
# Model description

Gradient boosting regressor trained on California Housing dataset

The model is a gradient boosting regressor from sklearn. On top of the standard
features, it contains predictions from a KNN models. These predictions are calculated
out of fold, then added on top of the existing features. These features are really
helpful for decision tree-based models, since those cannot easily learn from geospatial
data.

## Intended uses & limitations

This model is meant for demonstration purposes

## Training Procedure

### Hyperparameters

The model is trained with below 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

The model plot is below.

<style>#sk-container-id-13 {color: black;background-color: white;}#sk-container-id-13 pre{padding: 0;}#sk-container-id-13 div.sk-toggleable {background-color: white;}#sk-container-id-13 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-13 label.sk-toggleable__label-arrow:before {content: "▸";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-13 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-13 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-13 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-13 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-13 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-13 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: "▾";}#sk-container-id-13 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-13 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-13 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-13 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-13 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-13 div.sk-parallel-item::after {content: "";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-13 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-13 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-13 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-13 div.sk-item {position: relative;z-index: 1;}#sk-container-id-13 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-13 div.sk-item::before, #sk-container-id-13 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-13 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-13 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-13 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-13 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-13 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-13 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-13 div.sk-label-container {text-align: center;}#sk-container-id-13 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-13 div.sk-text-repr-fallback {display: none;}</style><div id="sk-container-id-13" 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-41" type="checkbox" ><label for="sk-estimator-id-41" 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-42" type="checkbox" ><label for="sk-estimator-id-42" 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-43" type="checkbox" ><label for="sk-estimator-id-43" 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-44" type="checkbox" ><label for="sk-estimator-id-44" 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-45" type="checkbox" ><label for="sk-estimator-id-45" 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-46" type="checkbox" ><label for="sk-estimator-id-46" 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

Metrics are calculated on the test set

| Metric                  |        Value |
|-------------------------|--------------|
| Root mean squared error | 44273.5      |
| Mean absolute error     | 30079.9      |
| R²                      |     0.805954 |

## Dataset description

California Housing dataset
--------------------------

**Data Set Characteristics:**

    :Number of Instances: 20640

    :Number of Attributes: 8 numeric, predictive attributes and the target

    :Attribute Information:
        - MedInc        median income in block group
        - HouseAge      median house age in block group
        - AveRooms      average number of rooms per household
        - AveBedrms     average number of bedrooms per household
        - Population    block group population
        - AveOccup      average number of household members
        - Latitude      block group latitude
        - Longitude     block group longitude

    :Missing Attribute Values: None

This dataset was obtained from the StatLib repository.
https://www.dcc.fc.up.pt/~ltorgo/Regression/cal_housing.html

The target variable is the median house value for California districts,
expressed in hundreds of thousands of dollars ($100,000).

This dataset was derived from the 1990 U.S. census, using one row per census
block group. A block group is the smallest geographical unit for which the U.S.
Census Bureau publishes sample data (a block group typically has a population
of 600 to 3,000 people).

An household is a group of people residing within a home. Since the average
number of rooms and bedrooms in this dataset are provided per household, these
columns may take surpinsingly large values for block groups with few households
and many empty houses, such as vacation resorts.

It can be downloaded/loaded using the
:func:`sklearn.datasets.fetch_california_housing` function.

.. topic:: References

    - Pace, R. Kelley and Ronald Barry, Sparse Spatial Autoregressions,
      Statistics and Probability Letters, 33 (1997) 291-297

### Data distribution

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

![Data distribution](geographic.png)

</details>

# How to Get Started with the Model

Run the code below to load the model

```python
import json
import pandas as pd
import skops.io as sio
model = sio.load("model.skops")
with open("config.json") as f:
    config = json.load(f)
model.predict(pd.DataFrame.from_dict(config["sklearn"]["example_input"]))
```

# Model Card Authors

Benjamin Bossan

# Model Card Contact

benjamin@huggingface.co

# Permutation Importances

![Permutation Importances](permutation-importances.png)

{"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]}}}
sklearn skops tabular-regression has_space region:us
TAGS
#sklearn #skops #tabular-regression #has_space #region-us 

Model description
=================


Gradient boosting regressor trained on California Housing dataset


The model is a gradient boosting regressor from sklearn. On top of the standard
features, it contains predictions from a KNN models. These predictions are calculated
out of fold, then added on top of the existing features. These features are really
helpful for decision tree-based models, since those cannot easily learn from geospatial
data.


Intended uses & limitations
---------------------------


This model is meant for demonstration purposes


Training Procedure
------------------


### Hyperparameters


The model is trained with below hyperparameters.



 Click to expand 


### Model Plot


The model plot is below.


#sk-container-id-13 {color: black;background-color: white;}#sk-container-id-13 pre{padding: 0;}#sk-container-id-13 URL-toggleable {background-color: white;}#sk-container-id-13 URL-toggleable\_\_label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-13 URL-toggleable\_\_label-arrow:before {content: "▸";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-13 URL-toggleable\_\_label-arrow:hover:before {color: black;}#sk-container-id-13 URL-estimator:hover URL-toggleable\_\_label-arrow:before {color: black;}#sk-container-id-13 URL-toggleable\_\_content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-13 URL-toggleable\_\_content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-13 URL-toggleable\_\_control:checked~URL-toggleable\_\_content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-13 URL-toggleable\_\_control:checked~URL-toggleable\_\_label-arrow:before {content: "▾";}#sk-container-id-13 URL-estimator URL-toggleable\_\_control:checked~URL-toggleable\_\_label {background-color: #d4ebff;}#sk-container-id-13 URL-label URL-toggleable\_\_control:checked~URL-toggleable\_\_label {background-color: #d4ebff;}#sk-container-id-13 URL-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-13 URL-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-13 URL-estimator:hover {background-color: #d4ebff;}#sk-container-id-13 URL-parallel-item::after {content: "";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-13 URL-label:hover URL-toggleable\_\_label {background-color: #d4ebff;}#sk-container-id-13 URL-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-13 URL-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-13 URL-item {position: relative;z-index: 1;}#sk-container-id-13 URL-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-13 URL-item::before, #sk-container-id-13 URL-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-13 URL-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-13 URL-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-13 URL-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-13 URL-parallel-item:only-child::after {width: 0;}#sk-container-id-13 URL-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-13 URL-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-13 URL-label-container {text-align: center;}#sk-container-id-13 URL-container {/\* jupyter's 'URL' sets '[hidden] { display: none; }' but URL 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 URL. See: URL \*/display: inline-block !important;position: relative;}#sk-container-id-13 URL-text-repr-fallback {display: none;}
```
StackingRegressor(estimators=[('knn@5',Pipeline(steps=[('select_cols',ColumnTransformer(transformers=[('long_and_lat','passthrough',['Longitude','Latitude'])])),('knn',KNeighborsRegressor())]))],final_estimator=GradientBoostingRegressor(n_estimators=500,random_state=0),passthrough=True)
```
**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 URL.**StackingRegressor
```
StackingRegressor(estimators=[('knn@5',Pipeline(steps=[('select_cols',ColumnTransformer(transformers=[('long_and_lat','passthrough',['Longitude','Latitude'])])),('knn',KNeighborsRegressor())]))],final_estimator=GradientBoostingRegressor(n_estimators=500,random_state=0),passthrough=True)
```
knn@5select\_cols: ColumnTransformer
```
ColumnTransformer(transformers=[('long_and_lat', 'passthrough',['Longitude', 'Latitude'])])
```
long\_and\_lat
```
['Longitude', 'Latitude']
```
passthrough
```
passthrough
```
KNeighborsRegressor
```
KNeighborsRegressor()
```
final\_estimatorGradientBoostingRegressor
```
GradientBoostingRegressor(n_estimators=500, random_state=0)
```

Evaluation Results
------------------


Metrics are calculated on the test set



Dataset description
-------------------


California Housing dataset
--------------------------


Data Set Characteristics:



```
:Number of Instances: 20640

:Number of Attributes: 8 numeric, predictive attributes and the target

:Attribute Information:
    - MedInc        median income in block group
    - HouseAge      median house age in block group
    - AveRooms      average number of rooms per household
    - AveBedrms     average number of bedrooms per household
    - Population    block group population
    - AveOccup      average number of household members
    - Latitude      block group latitude
    - Longitude     block group longitude

:Missing Attribute Values: None

```

This dataset was obtained from the StatLib repository.
URL


The target variable is the median house value for California districts,
expressed in hundreds of thousands of dollars ($100,000).


This dataset was derived from the 1990 U.S. census, using one row per census
block group. A block group is the smallest geographical unit for which the U.S.
Census Bureau publishes sample data (a block group typically has a population
of 600 to 3,000 people).


An household is a group of people residing within a home. Since the average
number of rooms and bedrooms in this dataset are provided per household, these
columns may take surpinsingly large values for block groups with few households
and many empty houses, such as vacation resorts.


It can be downloaded/loaded using the
:func:'sklearn.datasets.fetch\_california\_housing' function.


.. topic:: References



```
- Pace, R. Kelley and Ronald Barry, Sparse Spatial Autoregressions,
  Statistics and Probability Letters, 33 (1997) 291-297

```

### Data distribution



 Click to expand 
!Data distribution



How to Get Started with the Model
=================================


Run the code below to load the model


Model Card Authors
==================


Benjamin Bossan


Model Card Contact
==================


benjamin@URL


Permutation Importances
=======================


!Permutation Importances



### Hyperparameters


The model is trained with below hyperparameters.



 Click to expand ### Model Plot


The model plot is below. ### Data distribution



 Click to expand 
!Data distribution



How to Get Started with the Model
=================================


Run the code below to load the model


Model Card Authors
==================


Benjamin Bossan


Model Card Contact
==================


benjamin@URL


Permutation Importances
=======================


!Permutation Importances
TAGS
#sklearn #skops #tabular-regression #has_space #region-us 
 ### Hyperparameters


The model is trained with below hyperparameters.



 Click to expand ### Model Plot


The model plot is below. ### Data distribution



 Click to expand 
!Data distribution



How to Get Started with the Model
=================================


Run the code below to load the model


Model Card Authors
==================


Benjamin Bossan


Model Card Contact
==================


benjamin@URL


Permutation Importances
=======================


!Permutation Importances
TAGS
#sklearn #skops #tabular-regression #has_space #region-us 
### Hyperparameters


The model is trained with below hyperparameters.



 Click to expand### Model Plot


The model plot is below.### Data distribution



 Click to expand 
!Data distribution



How to Get Started with the Model
=================================


Run the code below to load the model


Model Card Authors
==================


Benjamin Bossan


Model Card Contact
==================


benjamin@URL


Permutation Importances
=======================


!Permutation Importances