
# Model description

[More Information Needed]

## Intended uses & limitations

[More Information Needed]

## Training Procedure

### Hyperparameters

The model is trained with below hyperparameters.

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

| Hyperparameter                  | Value                                                    |
|---------------------------------|----------------------------------------------------------|
| aggressive_elimination          | False                                                    |
| cv                              | 5                                                        |
| error_score                     | nan                                                      |
| estimator__categorical_features |                                                          |
| estimator__early_stopping       | auto                                                     |
| estimator__l2_regularization    | 0.0                                                      |
| estimator__learning_rate        | 0.1                                                      |
| estimator__loss                 | auto                                                     |
| estimator__max_bins             | 255                                                      |
| estimator__max_depth            |                                                          |
| estimator__max_iter             | 100                                                      |
| estimator__max_leaf_nodes       | 31                                                       |
| estimator__min_samples_leaf     | 20                                                       |
| estimator__monotonic_cst        |                                                          |
| estimator__n_iter_no_change     | 10                                                       |
| estimator__random_state         |                                                          |
| estimator__scoring              | loss                                                     |
| estimator__tol                  | 1e-07                                                    |
| estimator__validation_fraction  | 0.1                                                      |
| estimator__verbose              | 0                                                        |
| estimator__warm_start           | False                                                    |
| estimator                       | HistGradientBoostingClassifier()                         |
| factor                          | 3                                                        |
| max_resources                   | auto                                                     |
| min_resources                   | exhaust                                                  |
| n_jobs                          | -1                                                       |
| param_grid                      | {'max_leaf_nodes': [5, 10, 15], 'max_depth': [2, 5, 10]} |
| random_state                    | 42                                                       |
| refit                           | True                                                     |
| resource                        | n_samples                                                |
| return_train_score              | True                                                     |
| scoring                         |                                                          |
| verbose                         | 0                                                        |

</details>

### Model Plot

The model plot is below.

<style>#sk-3de79340-4ee5-4aee-9c89-b3b7696153ce {color: black;background-color: white;}#sk-3de79340-4ee5-4aee-9c89-b3b7696153ce pre{padding: 0;}#sk-3de79340-4ee5-4aee-9c89-b3b7696153ce div.sk-toggleable {background-color: white;}#sk-3de79340-4ee5-4aee-9c89-b3b7696153ce label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-3de79340-4ee5-4aee-9c89-b3b7696153ce label.sk-toggleable__label-arrow:before {content: "▸";float: left;margin-right: 0.25em;color: #696969;}#sk-3de79340-4ee5-4aee-9c89-b3b7696153ce label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-3de79340-4ee5-4aee-9c89-b3b7696153ce div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-3de79340-4ee5-4aee-9c89-b3b7696153ce div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-3de79340-4ee5-4aee-9c89-b3b7696153ce div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-3de79340-4ee5-4aee-9c89-b3b7696153ce input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-3de79340-4ee5-4aee-9c89-b3b7696153ce input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: "▾";}#sk-3de79340-4ee5-4aee-9c89-b3b7696153ce div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-3de79340-4ee5-4aee-9c89-b3b7696153ce div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-3de79340-4ee5-4aee-9c89-b3b7696153ce 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-3de79340-4ee5-4aee-9c89-b3b7696153ce 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-3de79340-4ee5-4aee-9c89-b3b7696153ce div.sk-estimator:hover {background-color: #d4ebff;}#sk-3de79340-4ee5-4aee-9c89-b3b7696153ce div.sk-parallel-item::after {content: "";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-3de79340-4ee5-4aee-9c89-b3b7696153ce div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-3de79340-4ee5-4aee-9c89-b3b7696153ce div.sk-serial::before {content: "";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 2em;bottom: 0;left: 50%;}#sk-3de79340-4ee5-4aee-9c89-b3b7696153ce div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;}#sk-3de79340-4ee5-4aee-9c89-b3b7696153ce div.sk-item {z-index: 1;}#sk-3de79340-4ee5-4aee-9c89-b3b7696153ce div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;}#sk-3de79340-4ee5-4aee-9c89-b3b7696153ce div.sk-parallel::before {content: "";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 2em;bottom: 0;left: 50%;}#sk-3de79340-4ee5-4aee-9c89-b3b7696153ce div.sk-parallel-item {display: flex;flex-direction: column;position: relative;background-color: white;}#sk-3de79340-4ee5-4aee-9c89-b3b7696153ce div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-3de79340-4ee5-4aee-9c89-b3b7696153ce div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-3de79340-4ee5-4aee-9c89-b3b7696153ce div.sk-parallel-item:only-child::after {width: 0;}#sk-3de79340-4ee5-4aee-9c89-b3b7696153ce div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;position: relative;}#sk-3de79340-4ee5-4aee-9c89-b3b7696153ce div.sk-label label {font-family: monospace;font-weight: bold;background-color: white;display: inline-block;line-height: 1.2em;}#sk-3de79340-4ee5-4aee-9c89-b3b7696153ce div.sk-label-container {position: relative;z-index: 2;text-align: center;}#sk-3de79340-4ee5-4aee-9c89-b3b7696153ce 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-3de79340-4ee5-4aee-9c89-b3b7696153ce div.sk-text-repr-fallback {display: none;}</style><div id="sk-3de79340-4ee5-4aee-9c89-b3b7696153ce" class="sk-top-container"><div class="sk-text-repr-fallback"><pre>HalvingGridSearchCV(estimator=HistGradientBoostingClassifier(), n_jobs=-1,param_grid={&#x27;max_depth&#x27;: [2, 5, 10],&#x27;max_leaf_nodes&#x27;: [5, 10, 15]},random_state=42)</pre><b>Please rerun this cell to show the HTML repr or trust the notebook.</b></div><div class="sk-container" hidden><div class="sk-item sk-dashed-wrapped"><div class="sk-label-container"><div class="sk-label sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="474afc8c-e67d-430c-9432-eedced794614" type="checkbox" ><label for="474afc8c-e67d-430c-9432-eedced794614" class="sk-toggleable__label sk-toggleable__label-arrow">HalvingGridSearchCV</label><div class="sk-toggleable__content"><pre>HalvingGridSearchCV(estimator=HistGradientBoostingClassifier(), n_jobs=-1,param_grid={&#x27;max_depth&#x27;: [2, 5, 10],&#x27;max_leaf_nodes&#x27;: [5, 10, 15]},random_state=42)</pre></div></div></div><div class="sk-parallel"><div class="sk-parallel-item"><div class="sk-item"><div class="sk-serial"><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="cf1d66b1-cfe8-40b1-b6e9-7a62640add17" type="checkbox" ><label for="cf1d66b1-cfe8-40b1-b6e9-7a62640add17" class="sk-toggleable__label sk-toggleable__label-arrow">HistGradientBoostingClassifier</label><div class="sk-toggleable__content"><pre>HistGradientBoostingClassifier()</pre></div></div></div></div></div></div></div></div></div></div>

## Evaluation Results

You can find the details about evaluation process and the evaluation results.



| Metric   | Value   |
|----------|---------|

# How to Get Started with the Model

Use the code below to get started with the model.

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

```python
[More Information Needed]
```

</details>




# 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]
```
{"library_name": "sklearn", "tags": ["sklearn", "skops", "tabular-classification"], "widget": {"structuredData": {"area error": [30.29, 96.05, 48.31], "compactness error": [0.01911, 0.01652, 0.01484], "concave points error": [0.01037, 0.0137, 0.01093], "concavity error": [0.02701, 0.02269, 0.02813], "fractal dimension error": [0.003586, 0.001698, 0.002461], "mean area": [481.9, 1130.0, 748.9], "mean compactness": [0.1058, 0.1029, 0.1223], "mean concave points": [0.03821, 0.07951, 0.08087], "mean concavity": [0.08005, 0.108, 0.1466], "mean fractal dimension": [0.06373, 0.05461, 0.05796], "mean perimeter": [81.09, 123.6, 101.7], "mean radius": [12.47, 18.94, 15.46], "mean smoothness": [0.09965, 0.09009, 0.1092], "mean symmetry": [0.1925, 0.1582, 0.1931], "mean texture": [18.6, 21.31, 19.48], "perimeter error": [2.497, 5.486, 3.094], "radius error": [0.3961, 0.7888, 0.4743], "smoothness error": [0.006953, 0.004444, 0.00624], "symmetry error": [0.01782, 0.01386, 0.01397], "texture error": [1.044, 0.7975, 0.7859], "worst area": [677.9, 1866.0, 1156.0], "worst compactness": [0.2378, 0.2336, 0.2394], "worst concave points": [0.1015, 0.1789, 0.1514], "worst concavity": [0.2671, 0.2687, 0.3791], "worst fractal dimension": [0.0875, 0.06589, 0.08019], "worst perimeter": [96.05, 165.9, 124.9], "worst radius": [14.97, 24.86, 19.26], "worst smoothness": [0.1426, 0.1193, 0.1546], "worst symmetry": [0.3014, 0.2551, 0.2837], "worst texture": [24.64, 26.58, 26.0]}}}
sklearn skops tabular-classification region:us
TAGS
#sklearn #skops #tabular-classification #region-us 

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


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


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


### Hyperparameters


The model is trained with below hyperparameters.



 Click to expand 


### Model Plot


The model plot is below.


#sk-3de79340-4ee5-4aee-9c89-b3b7696153ce {color: black;background-color: white;}#sk-3de79340-4ee5-4aee-9c89-b3b7696153ce pre{padding: 0;}#sk-3de79340-4ee5-4aee-9c89-b3b7696153ce URL-toggleable {background-color: white;}#sk-3de79340-4ee5-4aee-9c89-b3b7696153ce URL-toggleable\_\_label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-3de79340-4ee5-4aee-9c89-b3b7696153ce URL-toggleable\_\_label-arrow:before {content: "▸";float: left;margin-right: 0.25em;color: #696969;}#sk-3de79340-4ee5-4aee-9c89-b3b7696153ce URL-toggleable\_\_label-arrow:hover:before {color: black;}#sk-3de79340-4ee5-4aee-9c89-b3b7696153ce URL-estimator:hover URL-toggleable\_\_label-arrow:before {color: black;}#sk-3de79340-4ee5-4aee-9c89-b3b7696153ce URL-toggleable\_\_content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-3de79340-4ee5-4aee-9c89-b3b7696153ce URL-toggleable\_\_content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-3de79340-4ee5-4aee-9c89-b3b7696153ce URL-toggleable\_\_control:checked~URL-toggleable\_\_content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-3de79340-4ee5-4aee-9c89-b3b7696153ce URL-toggleable\_\_control:checked~URL-toggleable\_\_label-arrow:before {content: "▾";}#sk-3de79340-4ee5-4aee-9c89-b3b7696153ce URL-estimator URL-toggleable\_\_control:checked~URL-toggleable\_\_label {background-color: #d4ebff;}#sk-3de79340-4ee5-4aee-9c89-b3b7696153ce URL-label URL-toggleable\_\_control:checked~URL-toggleable\_\_label {background-color: #d4ebff;}#sk-3de79340-4ee5-4aee-9c89-b3b7696153ce 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-3de79340-4ee5-4aee-9c89-b3b7696153ce 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-3de79340-4ee5-4aee-9c89-b3b7696153ce URL-estimator:hover {background-color: #d4ebff;}#sk-3de79340-4ee5-4aee-9c89-b3b7696153ce URL-parallel-item::after {content: "";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-3de79340-4ee5-4aee-9c89-b3b7696153ce URL-label:hover URL-toggleable\_\_label {background-color: #d4ebff;}#sk-3de79340-4ee5-4aee-9c89-b3b7696153ce URL-serial::before {content: "";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 2em;bottom: 0;left: 50%;}#sk-3de79340-4ee5-4aee-9c89-b3b7696153ce URL-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;}#sk-3de79340-4ee5-4aee-9c89-b3b7696153ce URL-item {z-index: 1;}#sk-3de79340-4ee5-4aee-9c89-b3b7696153ce URL-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;}#sk-3de79340-4ee5-4aee-9c89-b3b7696153ce URL-parallel::before {content: "";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 2em;bottom: 0;left: 50%;}#sk-3de79340-4ee5-4aee-9c89-b3b7696153ce URL-parallel-item {display: flex;flex-direction: column;position: relative;background-color: white;}#sk-3de79340-4ee5-4aee-9c89-b3b7696153ce URL-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-3de79340-4ee5-4aee-9c89-b3b7696153ce URL-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-3de79340-4ee5-4aee-9c89-b3b7696153ce URL-parallel-item:only-child::after {width: 0;}#sk-3de79340-4ee5-4aee-9c89-b3b7696153ce 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;position: relative;}#sk-3de79340-4ee5-4aee-9c89-b3b7696153ce URL-label label {font-family: monospace;font-weight: bold;background-color: white;display: inline-block;line-height: 1.2em;}#sk-3de79340-4ee5-4aee-9c89-b3b7696153ce URL-label-container {position: relative;z-index: 2;text-align: center;}#sk-3de79340-4ee5-4aee-9c89-b3b7696153ce 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-3de79340-4ee5-4aee-9c89-b3b7696153ce URL-text-repr-fallback {display: none;}
```
HalvingGridSearchCV(estimator=HistGradientBoostingClassifier(), n_jobs=-1,param_grid={'max_depth': [2, 5, 10],'max_leaf_nodes': [5, 10, 15]},random_state=42)
```
**Please rerun this cell to show the HTML repr or trust the notebook.**HalvingGridSearchCV
```
HalvingGridSearchCV(estimator=HistGradientBoostingClassifier(), n_jobs=-1,param_grid={'max_depth': [2, 5, 10],'max_leaf_nodes': [5, 10, 15]},random_state=42)
```
HistGradientBoostingClassifier
```
HistGradientBoostingClassifier()
```

## Evaluation Results


You can find the details about evaluation process and the evaluation results.



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


Use the code below to get started with the model.



 Click to expand 

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


This model card is written by following authors:


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


You can contact the model card authors through following channels:


Below you can find information related to citation.


BibTeX:



### Hyperparameters


The model is trained with below hyperparameters.



 Click to expand ### Model Plot


The model plot is below.
TAGS
#sklearn #skops #tabular-classification #region-us 
 ### Hyperparameters


The model is trained with below hyperparameters.



 Click to expand ### Model Plot


The model plot is below.
passage: TAGS
#sklearn #skops #tabular-classification #region-us 
### Hyperparameters


The model is trained with below hyperparameters.



 Click to expand### Model Plot


The model plot is below.