| --- |
| library_name: sklearn |
| tags: |
| - sklearn |
| - skops |
| - tabular-classification |
| model_format: pickle |
| model_file: model.pkl |
| widget: |
| structuredData: |
| Month: |
| - 5.0 |
| - 4.0 |
| - 10.0 |
| Quantity: |
| - 4.0 |
| - 2.0 |
| - 2.0 |
| Seller: |
| - 8.0 |
| - 8.0 |
| - 3.0 |
| Total Cost: |
| - 2146.0 |
| - 4216.2 |
| - 9480.0 |
| Week: |
| - 17.0 |
| - 13.0 |
| - 40.0 |
| Year: |
| - 2022.0 |
| - 2022.0 |
| - 2022.0 |
| --- |
| |
| # Model description |
|
|
| [More Information Needed] |
|
|
| ## Intended uses & limitations |
|
|
| [More Information Needed] |
|
|
| ## Training Procedure |
|
|
| [More Information Needed] |
|
|
| ### Hyperparameters |
|
|
| <details> |
| <summary> Click to expand </summary> |
|
|
| | Hyperparameter | Value | |
| |------------------|-----------| |
| | algorithm | auto | |
| | leaf_size | 30 | |
| | metric | manhattan | |
| | metric_params | | |
| | n_jobs | | |
| | n_neighbors | 10 | |
| | p | 2 | |
| | weights | distance | |
|
|
| </details> |
|
|
| ### Model Plot |
|
|
| <style>#sk-container-id-13 {color: black;}#sk-container-id-13 pre{padding: 0;}#sk-container-id-13 div.sk-toggleable {background-color: white;}#sk-container-id-13 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-13 label.sk-toggleable__label-arrow:before {content: "▸";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-13 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-13 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-13 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-13 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-13 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-13 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: "▾";}#sk-container-id-13 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-13 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-13 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-13 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-13 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-13 div.sk-parallel-item::after {content: "";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-13 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-13 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-13 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-13 div.sk-item {position: relative;z-index: 1;}#sk-container-id-13 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-13 div.sk-item::before, #sk-container-id-13 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-13 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-13 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-13 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-13 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-13 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-13 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-13 div.sk-label-container {text-align: center;}#sk-container-id-13 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-13 div.sk-text-repr-fallback {display: none;}</style><div id="sk-container-id-13" class="sk-top-container" style="overflow: auto;"><div class="sk-text-repr-fallback"><pre>KNeighborsRegressor(metric='manhattan', n_neighbors=10, weights='distance')</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-13" type="checkbox" checked><label for="sk-estimator-id-13" class="sk-toggleable__label sk-toggleable__label-arrow">KNeighborsRegressor</label><div class="sk-toggleable__content"><pre>KNeighborsRegressor(metric='manhattan', n_neighbors=10, weights='distance')</pre></div></div></div></div></div> |
| |
| ## Evaluation Results |
| |
| | Metric | Value | |
| |-----------|---------| |
| | accuracy | 0.717 | |
| | r squared | 0.717 | |
| |
| # 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] |
| ``` |
| |
| # model_description |
| |
| This is a K-Nearest Neighbour Regressor trained to identify inventory-delay time. |
| |
| # limitations |
| |
| This model is trained for educational purposes. |
| |
