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---
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

<details>
<summary> Click to expand </summary>

| 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   |

</details>

### Model Plot

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## 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.