---
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.
Lasso(alpha=0.01, random_state=42)
## Evaluation Results
[More Information Needed]
# How to Get Started with the Model
```python
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.