---
library_name: sklearn
tags:
- sklearn
- skops
- tabular-regression
model_format: skops
model_file: model.skops
widget:
  structuredData:
    x0:
    - -0.8513550738681201
    - 0.3565756375241982
    - -0.5493723960200406
    x1:
    - -0.9801306786815437
    - 0.16144422497410207
    - -0.5044744688250247
    x2:
    - -0.40478372420423153
    - 0.465368421656243
    - -0.6223217606693501
    x3:
    - -0.5539725609683268
    - 0.3927870023121129
    - 1.2133119571551605
    x4:
    - -0.3313192794050237
    - -0.5263980861381337
    - 0.14244353694681483
    x5:
    - -0.6076784605515674
    - -0.3021390244014409
    - 0.37259389709675395
    x6:
    - 0.31079384041548314
    - -0.11643850592424994
    - -0.7648620670356181
    x7:
    - -0.7921692833892792
    - 0.5610338186827566
    - -0.707594089509777
---

# Model description

[More Information Needed]

## Intended uses & limitations

[More Information Needed]

## Training Procedure

### Hyperparameters

The model is trained with below hyperparameters.

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

| Hyperparameter    | Value   |
|-------------------|---------|
| C                 | 1.0     |
| class_weight      |         |
| dual              | False   |
| fit_intercept     | True    |
| intercept_scaling | 1       |
| l1_ratio          |         |
| max_iter          | 100     |
| multi_class       | auto    |
| n_jobs            |         |
| penalty           | l2      |
| random_state      | 0       |
| solver            | lbfgs   |
| tol               | 0.0001  |
| verbose           | 0       |
| warm_start        | False   |

</details>

### Model Plot

The model plot is below.

<style>#sk-90f04601-be1b-4c03-ba77-046c22963fb6 {color: black;background-color: white;}#sk-90f04601-be1b-4c03-ba77-046c22963fb6 pre{padding: 0;}#sk-90f04601-be1b-4c03-ba77-046c22963fb6 div.sk-toggleable {background-color: white;}#sk-90f04601-be1b-4c03-ba77-046c22963fb6 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-90f04601-be1b-4c03-ba77-046c22963fb6 label.sk-toggleable__label-arrow:before {content: "▸";float: left;margin-right: 0.25em;color: #696969;}#sk-90f04601-be1b-4c03-ba77-046c22963fb6 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-90f04601-be1b-4c03-ba77-046c22963fb6 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-90f04601-be1b-4c03-ba77-046c22963fb6 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-90f04601-be1b-4c03-ba77-046c22963fb6 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-90f04601-be1b-4c03-ba77-046c22963fb6 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-90f04601-be1b-4c03-ba77-046c22963fb6 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: "▾";}#sk-90f04601-be1b-4c03-ba77-046c22963fb6 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-90f04601-be1b-4c03-ba77-046c22963fb6 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-90f04601-be1b-4c03-ba77-046c22963fb6 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-90f04601-be1b-4c03-ba77-046c22963fb6 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-90f04601-be1b-4c03-ba77-046c22963fb6 div.sk-estimator:hover {background-color: #d4ebff;}#sk-90f04601-be1b-4c03-ba77-046c22963fb6 div.sk-parallel-item::after {content: "";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-90f04601-be1b-4c03-ba77-046c22963fb6 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-90f04601-be1b-4c03-ba77-046c22963fb6 div.sk-serial::before {content: "";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 2em;bottom: 0;left: 50%;}#sk-90f04601-be1b-4c03-ba77-046c22963fb6 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;}#sk-90f04601-be1b-4c03-ba77-046c22963fb6 div.sk-item {z-index: 1;}#sk-90f04601-be1b-4c03-ba77-046c22963fb6 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;}#sk-90f04601-be1b-4c03-ba77-046c22963fb6 div.sk-parallel::before {content: "";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 2em;bottom: 0;left: 50%;}#sk-90f04601-be1b-4c03-ba77-046c22963fb6 div.sk-parallel-item {display: flex;flex-direction: column;position: relative;background-color: white;}#sk-90f04601-be1b-4c03-ba77-046c22963fb6 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-90f04601-be1b-4c03-ba77-046c22963fb6 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-90f04601-be1b-4c03-ba77-046c22963fb6 div.sk-parallel-item:only-child::after {width: 0;}#sk-90f04601-be1b-4c03-ba77-046c22963fb6 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-90f04601-be1b-4c03-ba77-046c22963fb6 div.sk-label label {font-family: monospace;font-weight: bold;background-color: white;display: inline-block;line-height: 1.2em;}#sk-90f04601-be1b-4c03-ba77-046c22963fb6 div.sk-label-container {position: relative;z-index: 2;text-align: center;}#sk-90f04601-be1b-4c03-ba77-046c22963fb6 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-90f04601-be1b-4c03-ba77-046c22963fb6 div.sk-text-repr-fallback {display: none;}</style><div id="sk-90f04601-be1b-4c03-ba77-046c22963fb6" class="sk-top-container" style="overflow: auto;"><div class="sk-text-repr-fallback"><pre>LogisticRegression(random_state=0)</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="0f833999-8370-4e5a-b408-d110b9efa0de" type="checkbox" checked><label for="0f833999-8370-4e5a-b408-d110b9efa0de" class="sk-toggleable__label sk-toggleable__label-arrow">LogisticRegression</label><div class="sk-toggleable__content"><pre>LogisticRegression(random_state=0)</pre></div></div></div></div></div>

## Evaluation Results

You can find the details about evaluation process and the evaluation results.

| Metric         |    Value |
|----------------|----------|
| Train Accuracy | 0.791531 |
| Test Accuracy  | 0.714286 |

# How to Get Started with the Model

[More Information Needed]

# Model Card Authors

This model card is written by following authors:

[More Information Needed]

# 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]
```

# limitations

Mô hình chưa thể dùng trong production.

# model_description

Regression model thử nghiệm với skops.