---
license: mit
library_name: sklearn
tags:
- sklearn
- skops
- tabular-regression
widget:
structuredData:
AMBIENT_TEMPERATURE:
- 21.4322062
- 27.322759933333337
- 25.56246340000001
DAILY_YIELD:
- 0.0
- 996.4285714
- 685.0
DC_POWER:
- 0.0
- 8358.285714
- 6741.285714
IRRADIATION:
- 0.0
- 0.6465474886666664
- 0.498367802
MODULE_TEMPERATURE:
- 19.826896066666663
- 45.7407144
- 38.252356133333336
TOTAL_YIELD:
- 7218223.0
- 6366043.429
- 6372656.0
---
# Model description
This is a LinearRegression model trained on Solar Power Generation Data.
## Intended uses & limitations
This model is not ready to be used in production.
## Training Procedure
### Hyperparameters
The model is trained with below hyperparameters.
Click to expand
| Hyperparameter | Value |
|------------------|------------|
| alpha | 1.0 |
| copy_X | True |
| fit_intercept | True |
| l1_ratio | 0.5 |
| max_iter | 1000 |
| normalize | deprecated |
| positive | False |
| precompute | False |
| random_state | 0 |
| selection | cyclic |
| tol | 0.0001 |
| warm_start | False |
### Model Plot
The model plot is below.
ElasticNet(random_state=0)
## Evaluation Results
You can find the details about evaluation process and the evaluation results.
| Metric | Value |
|----------|---------|
| accuracy | 99.9994 |
# How to Get Started with the Model
Use the code below to get started with the model.
Click to expand
```python
import pickle
with open(dtc_pkl_filename, 'rb') as file:
clf = pickle.load(file)
```
# Model Card Authors
This model card is written by following authors:
ayyuce demirbas
# 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:**
```
bibtex
@inproceedings{...,year={2022}}
```