---
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 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
Click to expand
| Hyperparameter | Value |
|----------------------------------------------------------|--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| memory | |
| steps | [('columntransformer', ColumnTransformer(transformers=[('simpleimputer',
SimpleImputer(add_indicator=True),
('ordinalencoder',
OrdinalEncoder(encoded_missing_value=-2,
handle_unknown='use_encoded_value',
unknown_value=-1),
SimpleImputer(add_indicator=True),
('ordinalencoder',
OrdinalEncoder(encoded_missing_value=-2,
handle_unknown='use_encoded_value',
unknown_value=-1),
unknown_value=-1),
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 |
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.
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(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>)])
<sklearn.compose._column_transformer.make_column_selector object at 0x000001EF7028B6D0>
SimpleImputer(add_indicator=True)
<sklearn.compose._column_transformer.make_column_selector object at 0x000001EF252211B0>
OrdinalEncoder(encoded_missing_value=-2, handle_unknown='use_encoded_value',unknown_value=-1)
RandomForestRegressor(random_state=42)