Instructions to use ayyuce/my_solar_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Scikit-learn
How to use ayyuce/my_solar_model with Scikit-learn:
from skops.hub_utils import download from skops.io import load download("ayyuce/my_solar_model", "path_to_folder") # make sure model file is in skops format # if model is a pickle file, make sure it's from a source you trust model = load("path_to_folder/solar.pkl") - Notebooks
- Google Colab
- Kaggle
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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
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}}
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