ames-housing-random-forest-predictor

haizad/ames-housing-random-forest-predictor

ames-housing-random-forest-predictor is a tabular classification model from haizad. Use it for the tabular classification task on the model card, and read the license before you ship it in a product. It is set up for sklearn. The card lists the license as mit.

This is a random forest regression model trained on ames housing dataset from OpenML.

Downloads · 30 days

Access

Public

Updated Apr 16, 2023

Repo size

8.2 MB

Likes

Public

Hugging Face

Repo makeup

Click a slice to open those files.

.pkl8.2 MB · 99%

At a glance

Task: Tabular Classification
Library: sklearn
License: mit
Access: Public
Created: Apr 16, 2023
Updated: Apr 16, 2023
SHA: dd77ab99

Spaces

sklearn-docs/combine-predictors-using-stacking

Model card

From the Hugging Face model README

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

<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 0x000001EF7028B6D0>),<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 0x000001EF252211B0>)])), ('randomforestregressor', RandomForestRegressor(random_state=42))]
verbose	False
columntransformer	ColumnTransformer(transformers=[('simpleimputer',<br /> SimpleImputer(add_indicator=True),<br /> <sklearn.compose._column_transformer.make_column_selector object at 0x000001EF7028B6D0>),<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 0x000001EF252211B0>)])
randomforestregressor	RandomForestRegressor(random_state=42)
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 0x000001EF7028B6D0>), ('ordinalencoder', OrdinalEncoder(encoded_missing_value=-2, handle_unknown='use_encoded_value',<br /> unknown_value=-1), <sklearn.compose._column_transformer.make_column_selector object at 0x000001EF252211B0>)]
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
randomforestregressor__bootstrap	True
randomforestregressor__ccp_alpha	0.0
randomforestregressor__criterion	squared_error
randomforestregressor__max_depth
randomforestregressor__max_features	1.0
randomforestregressor__max_leaf_nodes
randomforestregressor__max_samples
randomforestregressor__min_impurity_decrease	0.0
randomforestregressor__min_samples_leaf	1
randomforestregressor__min_samples_split	2
randomforestregressor__min_weight_fraction_leaf	0.0
randomforestregressor__n_estimators	100
randomforestregressor__n_jobs
randomforestregressor__oob_score	False
randomforestregressor__random_state	42
randomforestregressor__verbose	0
randomforestregressor__warm_start	False

</details>

Model Plot

Evaluation Results

Metric	Value
R2 score	0.831021
MAE	0.111169

How to Get Started with the Model

Use the following code to get started:

import joblib
from skops.hub_utils import download
import json
import pandas as pd
download(repo_id="haizad/ames-housing-random-forest-predictor", dst='ames-housing-random-forest-predictor')
pipeline = joblib.load( "ames-housing-random-forest-predictor/model.pkl")
with open("ames-housing-random-forest-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

Task: Tabular Classification
Library: sklearn
License: mit
Created: Apr 16, 2023
Updated: Apr 16, 2023