california-housing / README.md
ruanmelio's picture
Upload folder using huggingface_hub
cb01e29 verified
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

Click to expand
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)
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

[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.