
# Model description

This is a random forest regression model trained on ames housing dataset from OpenML.

## Intended uses & limitations

This model is not ready to be used in production.

## Training Procedure

[More Information Needed]

### Hyperparameters

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

| Hyperparameter                                           | Value                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                        |
|----------------------------------------------------------|--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| memory                                                   |                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                              |
| steps                                                    | [('columntransformer', ColumnTransformer(transformers=[('simpleimputer',<br />                                 SimpleImputer(add_indicator=True),<br />                                 <sklearn.compose._column_transformer.make_column_selector object at 0x000001EF7028B6D0>),<br />                                ('ordinalencoder',<br />                                 OrdinalEncoder(encoded_missing_value=-2,<br />                                                handle_unknown='use_encoded_value',<br />                                                unknown_value=-1),<br />                                 <sklearn.compose._column_transformer.make_column_selector object at 0x000001EF252211B0>)])), ('randomforestregressor', RandomForestRegressor(random_state=42))] |
| verbose                                                  | False                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                        |
| columntransformer                                        | ColumnTransformer(transformers=[('simpleimputer',<br />                                 SimpleImputer(add_indicator=True),<br />                                 <sklearn.compose._column_transformer.make_column_selector object at 0x000001EF7028B6D0>),<br />                                ('ordinalencoder',<br />                                 OrdinalEncoder(encoded_missing_value=-2,<br />                                                handle_unknown='use_encoded_value',<br />                                                unknown_value=-1),<br />                                 <sklearn.compose._column_transformer.make_column_selector object at 0x000001EF252211B0>)])                                                                                             |
| randomforestregressor                                    | RandomForestRegressor(random_state=42)                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                       |
| columntransformer__n_jobs                                |                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                              |
| columntransformer__remainder                             | drop                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                         |
| columntransformer__sparse_threshold                      | 0.3                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                          |
| columntransformer__transformer_weights                   |                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                              |
| columntransformer__transformers                          | [('simpleimputer', SimpleImputer(add_indicator=True), <sklearn.compose._column_transformer.make_column_selector object at 0x000001EF7028B6D0>), ('ordinalencoder', OrdinalEncoder(encoded_missing_value=-2, handle_unknown='use_encoded_value',<br />               unknown_value=-1), <sklearn.compose._column_transformer.make_column_selector object at 0x000001EF252211B0>)]                                                                                                                                                                                                                                                                                                                                                                                  |
| columntransformer__verbose                               | False                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                        |
| columntransformer__verbose_feature_names_out             | True                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                         |
| columntransformer__simpleimputer                         | SimpleImputer(add_indicator=True)                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                            |
| columntransformer__ordinalencoder                        | OrdinalEncoder(encoded_missing_value=-2, handle_unknown='use_encoded_value',<br />               unknown_value=-1)                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                |
| columntransformer__simpleimputer__add_indicator          | True                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                         |
| columntransformer__simpleimputer__copy                   | True                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                         |
| columntransformer__simpleimputer__fill_value             |                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                              |
| columntransformer__simpleimputer__keep_empty_features    | False                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                        |
| columntransformer__simpleimputer__missing_values         | nan                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                          |
| columntransformer__simpleimputer__strategy               | mean                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                         |
| columntransformer__simpleimputer__verbose                | deprecated                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                   |
| columntransformer__ordinalencoder__categories            | auto                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                         |
| columntransformer__ordinalencoder__dtype                 | <class 'numpy.float64'>                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                      |
| columntransformer__ordinalencoder__encoded_missing_value | -2                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                           |
| columntransformer__ordinalencoder__handle_unknown        | use_encoded_value                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                            |
| columntransformer__ordinalencoder__unknown_value         | -1                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                           |
| randomforestregressor__bootstrap                         | True                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                         |
| randomforestregressor__ccp_alpha                         | 0.0                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                          |
| randomforestregressor__criterion                         | squared_error                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                |
| randomforestregressor__max_depth                         |                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                              |
| randomforestregressor__max_features                      | 1.0                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                          |
| randomforestregressor__max_leaf_nodes                    |                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                              |
| randomforestregressor__max_samples                       |                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                              |
| randomforestregressor__min_impurity_decrease             | 0.0                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                          |
| randomforestregressor__min_samples_leaf                  | 1                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                            |
| randomforestregressor__min_samples_split                 | 2                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                            |
| randomforestregressor__min_weight_fraction_leaf          | 0.0                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                          |
| randomforestregressor__n_estimators                      | 100                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                          |
| randomforestregressor__n_jobs                            |                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                              |
| randomforestregressor__oob_score                         | False                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                        |
| randomforestregressor__random_state                      | 42                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                           |
| randomforestregressor__verbose                           | 0                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                            |
| randomforestregressor__warm_start                        | False                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                        |

</details>

### Model Plot

<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: "▸";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: "▾";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: "";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: "";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: "";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id="sk-container-id-1" class="sk-top-container" style="overflow: auto;"><div class="sk-text-repr-fallback"><pre>Pipeline(steps=[(&#x27;columntransformer&#x27;,ColumnTransformer(transformers=[(&#x27;simpleimputer&#x27;,SimpleImputer(add_indicator=True),&lt;sklearn.compose._column_transformer.make_column_selector object at 0x000001EF7028B6D0&gt;),(&#x27;ordinalencoder&#x27;,OrdinalEncoder(encoded_missing_value=-2,handle_unknown=&#x27;use_encoded_value&#x27;,unknown_value=-1),&lt;sklearn.compose._column_transformer.make_column_selector object at 0x000001EF252211B0&gt;)])),(&#x27;randomforestregressor&#x27;,RandomForestRegressor(random_state=42))])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class="sk-container" hidden><div class="sk-item sk-dashed-wrapped"><div class="sk-label-container"><div class="sk-label sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-1" type="checkbox" ><label for="sk-estimator-id-1" class="sk-toggleable__label sk-toggleable__label-arrow">Pipeline</label><div class="sk-toggleable__content"><pre>Pipeline(steps=[(&#x27;columntransformer&#x27;,ColumnTransformer(transformers=[(&#x27;simpleimputer&#x27;,SimpleImputer(add_indicator=True),&lt;sklearn.compose._column_transformer.make_column_selector object at 0x000001EF7028B6D0&gt;),(&#x27;ordinalencoder&#x27;,OrdinalEncoder(encoded_missing_value=-2,handle_unknown=&#x27;use_encoded_value&#x27;,unknown_value=-1),&lt;sklearn.compose._column_transformer.make_column_selector object at 0x000001EF252211B0&gt;)])),(&#x27;randomforestregressor&#x27;,RandomForestRegressor(random_state=42))])</pre></div></div></div><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-2" type="checkbox" ><label for="sk-estimator-id-2" class="sk-toggleable__label sk-toggleable__label-arrow">columntransformer: ColumnTransformer</label><div class="sk-toggleable__content"><pre>ColumnTransformer(transformers=[(&#x27;simpleimputer&#x27;,SimpleImputer(add_indicator=True),&lt;sklearn.compose._column_transformer.make_column_selector object at 0x000001EF7028B6D0&gt;),(&#x27;ordinalencoder&#x27;,OrdinalEncoder(encoded_missing_value=-2,handle_unknown=&#x27;use_encoded_value&#x27;,unknown_value=-1),&lt;sklearn.compose._column_transformer.make_column_selector object at 0x000001EF252211B0&gt;)])</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-3" type="checkbox" ><label for="sk-estimator-id-3" class="sk-toggleable__label sk-toggleable__label-arrow">simpleimputer</label><div class="sk-toggleable__content"><pre>&lt;sklearn.compose._column_transformer.make_column_selector object at 0x000001EF7028B6D0&gt;</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-4" type="checkbox" ><label for="sk-estimator-id-4" class="sk-toggleable__label sk-toggleable__label-arrow">SimpleImputer</label><div class="sk-toggleable__content"><pre>SimpleImputer(add_indicator=True)</pre></div></div></div></div></div></div><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-5" type="checkbox" ><label for="sk-estimator-id-5" class="sk-toggleable__label sk-toggleable__label-arrow">ordinalencoder</label><div class="sk-toggleable__content"><pre>&lt;sklearn.compose._column_transformer.make_column_selector object at 0x000001EF252211B0&gt;</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-6" type="checkbox" ><label for="sk-estimator-id-6" class="sk-toggleable__label sk-toggleable__label-arrow">OrdinalEncoder</label><div class="sk-toggleable__content"><pre>OrdinalEncoder(encoded_missing_value=-2, handle_unknown=&#x27;use_encoded_value&#x27;,unknown_value=-1)</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-7" type="checkbox" ><label for="sk-estimator-id-7" class="sk-toggleable__label sk-toggleable__label-arrow">RandomForestRegressor</label><div class="sk-toggleable__content"><pre>RandomForestRegressor(random_state=42)</pre></div></div></div></div></div></div></div>

## Evaluation Results

| Metric   |    Value |
|----------|----------|
| R2 score | 0.831021 |
| MAE      | 0.111169 |

# How to Get Started with the Model

Use the following code to get started:

```python
import joblib
from skops.hub_utils import download
import json
import pandas as pd
download(repo_id="haizad/ames-housing-random-forest-predictor", dst='ames-housing-random-forest-predictor')
pipeline = joblib.load( "ames-housing-random-forest-predictor/model.pkl")
with open("ames-housing-random-forest-predictor/config.json") as f:
    config = json.load(f)
pipeline.predict(pd.DataFrame.from_dict(config["sklearn"]["example_input"]))
```

# 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]
```

# Intended uses & limitations

This model is not ready to be used in production.

# Evaluation

![Evaluation](evaluation.png)

{"license": "mit", "library_name": "sklearn", "tags": ["sklearn", "skops", "tabular-classification"], "model_format": "pickle", "model_file": "model.pkl", "widget": {"structuredData": {"BsmtFinSF1": [1280, 1464, 0], "BsmtUnfSF": [402, 536, 795], "Condition2": ["Norm", "Norm", "Norm"], "ExterQual": ["Ex", "Gd", "Gd"], "Foundation": ["PConc", "PConc", "PConc"], "GarageCars": [3, 3, 1], "GarageType": ["BuiltIn", "Attchd", "Detchd"], "Heating": ["GasA", "GasA", "GasA"], "HeatingQC": ["Ex", "Ex", "TA"], "HouseStyle": ["2Story", "1Story", "2.5Fin"], "MSSubClass": [60, 20, 75], "MasVnrArea": [272.0, 246.0, 0.0], "MasVnrType": ["Stone", "Stone", NaN], "MiscFeature": [NaN, NaN, NaN], "MoSold": [8, 7, 3], "OverallQual": [10, 8, 4], "Street": ["Pave", "Pave", "Pave"], "TotalBsmtSF": [1682, 2000, 795], "YearRemodAdd": [2008, 2005, 1950], "YrSold": [2008, 2007, 2006]}}}
sklearn skops tabular-classification license:mit has_space region:us
TAGS
#sklearn #skops #tabular-classification #license-mit #has_space #region-us 

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


This is a random forest regression model trained on ames housing dataset from OpenML.


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


This model is not ready to be used in production.


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


### Hyperparameters



 Click to expand 


### Model Plot


#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 URL-toggleable {background-color: white;}#sk-container-id-1 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-1 URL-toggleable\_\_label-arrow:before {content: "▸";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 URL-toggleable\_\_label-arrow:hover:before {color: black;}#sk-container-id-1 URL-estimator:hover URL-toggleable\_\_label-arrow:before {color: black;}#sk-container-id-1 URL-toggleable\_\_content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 URL-toggleable\_\_content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 URL-toggleable\_\_control:checked~URL-toggleable\_\_content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 URL-toggleable\_\_control:checked~URL-toggleable\_\_label-arrow:before {content: "▾";}#sk-container-id-1 URL-estimator URL-toggleable\_\_control:checked~URL-toggleable\_\_label {background-color: #d4ebff;}#sk-container-id-1 URL-label URL-toggleable\_\_control:checked~URL-toggleable\_\_label {background-color: #d4ebff;}#sk-container-id-1 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-1 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-1 URL-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 URL-parallel-item::after {content: "";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 URL-label:hover URL-toggleable\_\_label {background-color: #d4ebff;}#sk-container-id-1 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-1 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-1 URL-item {position: relative;z-index: 1;}#sk-container-id-1 URL-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 URL-item::before, #sk-container-id-1 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-1 URL-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 URL-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 URL-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 URL-parallel-item:only-child::after {width: 0;}#sk-container-id-1 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-1 URL-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 URL-label-container {text-align: center;}#sk-container-id-1 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-1 URL-text-repr-fallback {display: none;}
```
Pipeline(steps=[('columntransformer',ColumnTransformer(transformers=[('simpleimputer',SimpleImputer(add_indicator=True),<sklearn.compose._column_transformer.make_column_selector object at 0x000001EF7028B6D0>),('ordinalencoder',OrdinalEncoder(encoded_missing_value=-2,handle_unknown='use_encoded_value',unknown_value=-1),<sklearn.compose._column_transformer.make_column_selector object at 0x000001EF252211B0>)])),('randomforestregressor',RandomForestRegressor(random_state=42))])
```
**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.**Pipeline
```
Pipeline(steps=[('columntransformer',ColumnTransformer(transformers=[('simpleimputer',SimpleImputer(add_indicator=True),<sklearn.compose._column_transformer.make_column_selector object at 0x000001EF7028B6D0>),('ordinalencoder',OrdinalEncoder(encoded_missing_value=-2,handle_unknown='use_encoded_value',unknown_value=-1),<sklearn.compose._column_transformer.make_column_selector object at 0x000001EF252211B0>)])),('randomforestregressor',RandomForestRegressor(random_state=42))])
```
columntransformer: ColumnTransformer
```
ColumnTransformer(transformers=[('simpleimputer',SimpleImputer(add_indicator=True),<sklearn.compose._column_transformer.make_column_selector object at 0x000001EF7028B6D0>),('ordinalencoder',OrdinalEncoder(encoded_missing_value=-2,handle_unknown='use_encoded_value',unknown_value=-1),<sklearn.compose._column_transformer.make_column_selector object at 0x000001EF252211B0>)])
```
simpleimputer
```
<sklearn.compose._column_transformer.make_column_selector object at 0x000001EF7028B6D0>
```
SimpleImputer
```
SimpleImputer(add_indicator=True)
```
ordinalencoder
```
<sklearn.compose._column_transformer.make_column_selector object at 0x000001EF252211B0>
```
OrdinalEncoder
```
OrdinalEncoder(encoded_missing_value=-2, handle_unknown='use_encoded_value',unknown_value=-1)
```
RandomForestRegressor
```
RandomForestRegressor(random_state=42)
```

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



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


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==================


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Intended uses & limitations
===========================


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Evaluation
==========


!Evaluation



### Hyperparameters



 Click to expand ### Model Plot
TAGS
#sklearn #skops #tabular-classification #license-mit #has_space #region-us 
 ### Hyperparameters



 Click to expand ### Model Plot
TAGS
#sklearn #skops #tabular-classification #license-mit #has_space #region-us 
### Hyperparameters



 Click to expand### Model Plot