| | --- |
| | 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: |
| | - 1.59019987177207 |
| | - -0.6288517674735005 |
| | - -0.6288517674735005 |
| | x8: |
| | - -0.9276885579794873 |
| | - 1.0779479723001086 |
| | - 1.0779479723001086 |
| | x9: |
| | - -0.5836191855240799 |
| | - -0.5836191855240799 |
| | - -0.5836191855240799 |
| | --- |
| | |
| | # 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-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: "▸";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: "▾";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 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-container-id-1 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-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: "";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: "";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: "";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 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;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 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-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id="sk-container-id-1" class="sk-top-container" style="overflow: auto;"><div class="sk-text-repr-fallback"><pre>LogisticRegression(random_state=0)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</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="sk-estimator-id-1" type="checkbox" checked><label for="sk-estimator-id-1" 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 | 0.79316 | |
| | | Test Accuracy | 0.727273 | |
| | |
| | # 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. |
| | |
