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---
license: mit
library_name: sklearn
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---
# Model description
This is a LogisticRegressionCV model trained on averages of patch embeddings from the Imagenette dataset..
## Intended uses & limitations
This model is not intended to be used in production.
## Training Procedure
### Hyperparameters
The model is trained with below hyperparameters.
<details>
<summary> Click to expand </summary>
| Hyperparameter | Value |
|-------------------|-----------------------------------------------------------|
| Cs | 10 |
| class_weight | |
| cv | StratifiedKFold(n_splits=5, random_state=1, shuffle=True) |
| dual | False |
| fit_intercept | True |
| intercept_scaling | 1.0 |
| l1_ratios | |
| max_iter | 100 |
| multi_class | auto |
| n_jobs | |
| penalty | l2 |
| random_state | 1 |
| refit | False |
| scoring | |
| solver | lbfgs |
| tol | 0.0001 |
| verbose | 0 |
</details>
### Model Plot
The model plot is below.
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See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-58b9b78a-229c-4a6a-b67b-244945cdc29d div.sk-text-repr-fallback {display: none;}</style><div id="sk-58b9b78a-229c-4a6a-b67b-244945cdc29d" class="sk-top-container"><div class="sk-text-repr-fallback"><pre>LogisticRegressionCV(cv=StratifiedKFold(n_splits=5, random_state=1, shuffle=True),random_state=1, refit=False)</pre><b>Please rerun this cell to show the HTML repr or trust the notebook.</b></div><div class="sk-container" hidden><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="d612eebc-39a3-42fc-99e0-37e6f258ac21" type="checkbox" checked><label for="d612eebc-39a3-42fc-99e0-37e6f258ac21" class="sk-toggleable__label sk-toggleable__label-arrow">LogisticRegressionCV</label><div class="sk-toggleable__content"><pre>LogisticRegressionCV(cv=StratifiedKFold(n_splits=5, random_state=1, shuffle=True),random_state=1, refit=False)</pre></div></div></div></div></div>
## Evaluation Results
You can find the details about evaluation process and the evaluation results.
| Metric | Value |
|----------|---------|
| accuracy | 0.97707 |
| f1 score | 0.97707 |
# How to Get Started with the Model
Use the code below to get started with the model.
<details>
<summary> Click to expand </summary>
```python
import pickle
with open('model.pkl', 'rb') as file:
clf = pickle.load(file)
```
</details>
# Model Card Authors
This model card is written by following authors:
Patrick Ramos
# 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]
```
# Additional Content
## confusion_matrix
 |