You can deploy this model and start earning money today!

haizad/
ames-housing-gbdt-predictor

Name: ames-housing-gbdt-predictor
Availability: InStock

sklearn

skops

tabular-classification

mit

Views

No views yet

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

Click to expand

Hyperparameter	Value
memory
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))]
verbose	False
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)
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', 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', 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

Model Plot

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))])

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 nbviewer.org.

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.