|
--- |
|
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 |
|
--- |
|
|
|
# Model description |
|
|
|
This is a gradient boosted 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 0x000002A2B7A2B730>),<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 0x000002A2EC9B9180>)])), ('histgradientboostingregressor', HistGradientBoostingRegressor(random_state=0))] | |
|
| verbose | False | |
|
| columntransformer | ColumnTransformer(transformers=[('simpleimputer',<br /> SimpleImputer(add_indicator=True),<br /> <sklearn.compose._column_transformer.make_column_selector object at 0x000002A2B7A2B730>),<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 0x000002A2EC9B9180>)]) | |
|
| histgradientboostingregressor | HistGradientBoostingRegressor(random_state=0) | |
|
| 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 0x000002A2B7A2B730>), ('ordinalencoder', OrdinalEncoder(encoded_missing_value=-2, handle_unknown='use_encoded_value',<br /> unknown_value=-1), <sklearn.compose._column_transformer.make_column_selector object at 0x000002A2EC9B9180>)] | |
|
| 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 | |
|
| histgradientboostingregressor__categorical_features | | |
|
| histgradientboostingregressor__early_stopping | auto | |
|
| histgradientboostingregressor__interaction_cst | | |
|
| histgradientboostingregressor__l2_regularization | 0.0 | |
|
| histgradientboostingregressor__learning_rate | 0.1 | |
|
| histgradientboostingregressor__loss | squared_error | |
|
| histgradientboostingregressor__max_bins | 255 | |
|
| histgradientboostingregressor__max_depth | | |
|
| histgradientboostingregressor__max_iter | 100 | |
|
| histgradientboostingregressor__max_leaf_nodes | 31 | |
|
| histgradientboostingregressor__min_samples_leaf | 20 | |
|
| histgradientboostingregressor__monotonic_cst | | |
|
| histgradientboostingregressor__n_iter_no_change | 10 | |
|
| histgradientboostingregressor__quantile | | |
|
| histgradientboostingregressor__random_state | 0 | |
|
| histgradientboostingregressor__scoring | loss | |
|
| histgradientboostingregressor__tol | 1e-07 | |
|
| histgradientboostingregressor__validation_fraction | 0.1 | |
|
| histgradientboostingregressor__verbose | 0 | |
|
| histgradientboostingregressor__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=[('columntransformer',ColumnTransformer(transformers=[('simpleimputer',SimpleImputer(add_indicator=True),<sklearn.compose._column_transformer.make_column_selector object at 0x000002A2B7A2B730>),('ordinalencoder',OrdinalEncoder(encoded_missing_value=-2,handle_unknown='use_encoded_value',unknown_value=-1),<sklearn.compose._column_transformer.make_column_selector object at 0x000002A2EC9B9180>)])),('histgradientboostingregressor',HistGradientBoostingRegressor(random_state=0))])</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=[('columntransformer',ColumnTransformer(transformers=[('simpleimputer',SimpleImputer(add_indicator=True),<sklearn.compose._column_transformer.make_column_selector object at 0x000002A2B7A2B730>),('ordinalencoder',OrdinalEncoder(encoded_missing_value=-2,handle_unknown='use_encoded_value',unknown_value=-1),<sklearn.compose._column_transformer.make_column_selector object at 0x000002A2EC9B9180>)])),('histgradientboostingregressor',HistGradientBoostingRegressor(random_state=0))])</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=[('simpleimputer',SimpleImputer(add_indicator=True),<sklearn.compose._column_transformer.make_column_selector object at 0x000002A2B7A2B730>),('ordinalencoder',OrdinalEncoder(encoded_missing_value=-2,handle_unknown='use_encoded_value',unknown_value=-1),<sklearn.compose._column_transformer.make_column_selector object at 0x000002A2EC9B9180>)])</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><sklearn.compose._column_transformer.make_column_selector object at 0x000002A2B7A2B730></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><sklearn.compose._column_transformer.make_column_selector object at 0x000002A2EC9B9180></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='use_encoded_value',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">HistGradientBoostingRegressor</label><div class="sk-toggleable__content"><pre>HistGradientBoostingRegressor(random_state=0)</pre></div></div></div></div></div></div></div> |
|
|
|
## Evaluation Results |
|
|
|
| Metric | Value | |
|
|----------|----------| |
|
| R2 score | 0.838471 | |
|
| MAE | 0.111495 | |
|
|
|
# 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-gbdt-predictor", dst='ames-housing-gbdt-predictor') |
|
pipeline = joblib.load( "ames-housing-gbdt-predictor/model.pkl") |
|
with open("ames-housing-gbdt-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 |
|
|
|
 |
|
|
