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---
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 Lasso 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=[('pipeline',<br /> Pipeline(steps=[('standardscaler',<br /> StandardScaler()),<br /> ('simpleimputer',<br /> SimpleImputer(add_indicator=True))]),<br /> <sklearn.compose._column_transformer.make_column_selector object at 0x000001CF5D97B7C0>),<br /> ('onehotencoder',<br /> OneHotEncoder(handle_unknown='ignore'),<br /> <sklearn.compose._column_transformer.make_column_selector object at 0x000001CF128511E0>)])), ('lassocv', LassoCV())] |
| verbose | False |
| columntransformer | ColumnTransformer(transformers=[('pipeline',<br /> Pipeline(steps=[('standardscaler',<br /> StandardScaler()),<br /> ('simpleimputer',<br /> SimpleImputer(add_indicator=True))]),<br /> <sklearn.compose._column_transformer.make_column_selector object at 0x000001CF5D97B7C0>),<br /> ('onehotencoder',<br /> OneHotEncoder(handle_unknown='ignore'),<br /> <sklearn.compose._column_transformer.make_column_selector object at 0x000001CF128511E0>)]) |
| lassocv | LassoCV() |
| columntransformer__n_jobs | |
| columntransformer__remainder | drop |
| columntransformer__sparse_threshold | 0.3 |
| columntransformer__transformer_weights | |
| columntransformer__transformers | [('pipeline', Pipeline(steps=[('standardscaler', StandardScaler()),<br /> ('simpleimputer', SimpleImputer(add_indicator=True))]), <sklearn.compose._column_transformer.make_column_selector object at 0x000001CF5D97B7C0>), ('onehotencoder', OneHotEncoder(handle_unknown='ignore'), <sklearn.compose._column_transformer.make_column_selector object at 0x000001CF128511E0>)] |
| columntransformer__verbose | False |
| columntransformer__verbose_feature_names_out | True |
| columntransformer__pipeline | Pipeline(steps=[('standardscaler', StandardScaler()),<br /> ('simpleimputer', SimpleImputer(add_indicator=True))]) |
| columntransformer__onehotencoder | OneHotEncoder(handle_unknown='ignore') |
| columntransformer__pipeline__memory | |
| columntransformer__pipeline__steps | [('standardscaler', StandardScaler()), ('simpleimputer', SimpleImputer(add_indicator=True))] |
| columntransformer__pipeline__verbose | False |
| columntransformer__pipeline__standardscaler | StandardScaler() |
| columntransformer__pipeline__simpleimputer | SimpleImputer(add_indicator=True) |
| columntransformer__pipeline__standardscaler__copy | True |
| columntransformer__pipeline__standardscaler__with_mean | True |
| columntransformer__pipeline__standardscaler__with_std | True |
| columntransformer__pipeline__simpleimputer__add_indicator | True |
| columntransformer__pipeline__simpleimputer__copy | True |
| columntransformer__pipeline__simpleimputer__fill_value | |
| columntransformer__pipeline__simpleimputer__keep_empty_features | False |
| columntransformer__pipeline__simpleimputer__missing_values | nan |
| columntransformer__pipeline__simpleimputer__strategy | mean |
| columntransformer__pipeline__simpleimputer__verbose | deprecated |
| columntransformer__onehotencoder__categories | auto |
| columntransformer__onehotencoder__drop | |
| columntransformer__onehotencoder__dtype | <class 'numpy.float64'> |
| columntransformer__onehotencoder__handle_unknown | ignore |
| columntransformer__onehotencoder__max_categories | |
| columntransformer__onehotencoder__min_frequency | |
| columntransformer__onehotencoder__sparse | deprecated |
| columntransformer__onehotencoder__sparse_output | True |
| lassocv__alphas | |
| lassocv__copy_X | True |
| lassocv__cv | |
| lassocv__eps | 0.001 |
| lassocv__fit_intercept | True |
| lassocv__max_iter | 1000 |
| lassocv__n_alphas | 100 |
| lassocv__n_jobs | |
| lassocv__positive | False |
| lassocv__precompute | auto |
| lassocv__random_state | |
| lassocv__selection | cyclic |
| lassocv__tol | 0.0001 |
| lassocv__verbose | 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=[('pipeline',Pipeline(steps=[('standardscaler',StandardScaler()),('simpleimputer',SimpleImputer(add_indicator=True))]),<sklearn.compose._column_transformer.make_column_selector object at 0x000001CF5D97B7C0>),('onehotencoder',OneHotEncoder(handle_unknown='ignore'),<sklearn.compose._column_transformer.make_column_selector object at 0x000001CF128511E0>)])),('lassocv', LassoCV())])</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=[('pipeline',Pipeline(steps=[('standardscaler',StandardScaler()),('simpleimputer',SimpleImputer(add_indicator=True))]),<sklearn.compose._column_transformer.make_column_selector object at 0x000001CF5D97B7C0>),('onehotencoder',OneHotEncoder(handle_unknown='ignore'),<sklearn.compose._column_transformer.make_column_selector object at 0x000001CF128511E0>)])),('lassocv', LassoCV())])</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=[('pipeline',Pipeline(steps=[('standardscaler',StandardScaler()),('simpleimputer',SimpleImputer(add_indicator=True))]),<sklearn.compose._column_transformer.make_column_selector object at 0x000001CF5D97B7C0>),('onehotencoder',OneHotEncoder(handle_unknown='ignore'),<sklearn.compose._column_transformer.make_column_selector object at 0x000001CF128511E0>)])</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">pipeline</label><div class="sk-toggleable__content"><pre><sklearn.compose._column_transformer.make_column_selector object at 0x000001CF5D97B7C0></pre></div></div></div><div class="sk-serial"><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="sk-estimator-id-4" type="checkbox" ><label for="sk-estimator-id-4" class="sk-toggleable__label sk-toggleable__label-arrow">StandardScaler</label><div class="sk-toggleable__content"><pre>StandardScaler()</pre></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-5" type="checkbox" ><label for="sk-estimator-id-5" 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></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-6" type="checkbox" ><label for="sk-estimator-id-6" class="sk-toggleable__label sk-toggleable__label-arrow">onehotencoder</label><div class="sk-toggleable__content"><pre><sklearn.compose._column_transformer.make_column_selector object at 0x000001CF128511E0></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-7" type="checkbox" ><label for="sk-estimator-id-7" class="sk-toggleable__label sk-toggleable__label-arrow">OneHotEncoder</label><div class="sk-toggleable__content"><pre>OneHotEncoder(handle_unknown='ignore')</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-8" type="checkbox" ><label for="sk-estimator-id-8" class="sk-toggleable__label sk-toggleable__label-arrow">LassoCV</label><div class="sk-toggleable__content"><pre>LassoCV()</pre></div></div></div></div></div></div></div>
## Evaluation Results
| Metric | Value |
|----------|----------|
| R2 score | 0.753308 |
| MAE | 0.112742 |
# 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-lasso-predictor", dst='ames-housing-lasso-predictor')
pipeline = joblib.load( "ames-housing-lasso-predictor/model.pkl")
with open("ames-housing-lasso-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]
```
# Evaluation
|