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)In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
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Lasso(alpha=0.01, random_state=42)
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
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BibTeX:
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Intended uses & limitations
This model is made for the purposes of showing how to use Highwind only.
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