metadata

library_name: sklearn
tags:
  - sklearn
  - tabular-regression

Model description

This is a linear regression model trained on California housing dataset. This model could be used to predict median price of a house in California, given certain features. This model is very basic and should only be used as an example of how to use Highwind.

Intended uses & limitations

This model is made for the purposes of showing how to use Highwind only.

Training Procedure

[More Information Needed]

Hyperparameters

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Hyperparameter	Value
alpha	0.01
copy_X	True
fit_intercept	True
max_iter	1000
positive	False
precompute	False
random_state	42
selection	cyclic
tol	0.0001
warm_start	False

Model Plot

Lasso(alpha=0.01, random_state=42)

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Evaluation Results

[More Information Needed]

How to Get Started with the Model

import joblib
from huggingface_hub import hf_hub_download

# Feature scaler
hf_hub_download("MelioAI/california-housing", "scaler.joblib")
scaler = joblib.load("scaler.joblib")

# Classifier model
hf_hub_download("MelioAI/california-housing", "model.joblib")
model = joblib.load("model.joblib")

Model Card Authors

MelioAI, ruanmelio

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 made for the purposes of showing how to use Highwind only.