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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 meant 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.
<style>#sk-895fd056-3abd-4fd8-b159-0cd0313205d1 {color: black;background-color: white;}#sk-895fd056-3abd-4fd8-b159-0cd0313205d1 pre{padding: 0;}#sk-895fd056-3abd-4fd8-b159-0cd0313205d1 div.sk-toggleable {background-color: white;}#sk-895fd056-3abd-4fd8-b159-0cd0313205d1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-895fd056-3abd-4fd8-b159-0cd0313205d1 label.sk-toggleable__label-arrow:before {content: "▸";float: left;margin-right: 0.25em;color: #696969;}#sk-895fd056-3abd-4fd8-b159-0cd0313205d1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-895fd056-3abd-4fd8-b159-0cd0313205d1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-895fd056-3abd-4fd8-b159-0cd0313205d1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-895fd056-3abd-4fd8-b159-0cd0313205d1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-895fd056-3abd-4fd8-b159-0cd0313205d1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-895fd056-3abd-4fd8-b159-0cd0313205d1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: "▾";}#sk-895fd056-3abd-4fd8-b159-0cd0313205d1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-895fd056-3abd-4fd8-b159-0cd0313205d1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-895fd056-3abd-4fd8-b159-0cd0313205d1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-895fd056-3abd-4fd8-b159-0cd0313205d1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-895fd056-3abd-4fd8-b159-0cd0313205d1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-895fd056-3abd-4fd8-b159-0cd0313205d1 div.sk-parallel-item::after {content: "";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-895fd056-3abd-4fd8-b159-0cd0313205d1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-895fd056-3abd-4fd8-b159-0cd0313205d1 div.sk-serial::before {content: "";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 2em;bottom: 0;left: 50%;}#sk-895fd056-3abd-4fd8-b159-0cd0313205d1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;}#sk-895fd056-3abd-4fd8-b159-0cd0313205d1 div.sk-item {z-index: 1;}#sk-895fd056-3abd-4fd8-b159-0cd0313205d1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;}#sk-895fd056-3abd-4fd8-b159-0cd0313205d1 div.sk-parallel::before {content: "";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 2em;bottom: 0;left: 50%;}#sk-895fd056-3abd-4fd8-b159-0cd0313205d1 div.sk-parallel-item {display: flex;flex-direction: column;position: relative;background-color: white;}#sk-895fd056-3abd-4fd8-b159-0cd0313205d1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-895fd056-3abd-4fd8-b159-0cd0313205d1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-895fd056-3abd-4fd8-b159-0cd0313205d1 div.sk-parallel-item:only-child::after {width: 0;}#sk-895fd056-3abd-4fd8-b159-0cd0313205d1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;position: relative;}#sk-895fd056-3abd-4fd8-b159-0cd0313205d1 div.sk-label label {font-family: monospace;font-weight: bold;background-color: white;display: inline-block;line-height: 1.2em;}#sk-895fd056-3abd-4fd8-b159-0cd0313205d1 div.sk-label-container {position: relative;z-index: 2;text-align: center;}#sk-895fd056-3abd-4fd8-b159-0cd0313205d1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-895fd056-3abd-4fd8-b159-0cd0313205d1 div.sk-text-repr-fallback {display: none;}</style><div id="sk-895fd056-3abd-4fd8-b159-0cd0313205d1" 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="84c6f44b-cb50-41c4-bb4f-7803b113bf0c" type="checkbox" checked><label for="84c6f44b-cb50-41c4-bb4f-7803b113bf0c" 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]
``` |