| --- |
| library_name: sklearn |
| tags: |
| - sklearn |
| - skops |
| - tabular-regression |
| model_file: skops47mqlzp0 |
| widget: |
| structuredData: |
| acceleration: |
| - 12.0 |
| - 19.0 |
| - 20.7 |
| cylinders: |
| - 8 |
| - 4 |
| - 4 |
| displacement: |
| - 307.0 |
| - 97.0 |
| - 98.0 |
| horsepower: |
| - '130' |
| - '88' |
| - '65' |
| model year: |
| - 70 |
| - 73 |
| - 81 |
| origin: |
| - 1 |
| - 3 |
| - 1 |
| weight: |
| - 3504 |
| - 2279 |
| - 2380 |
| --- |
| |
| # Model description |
|
|
| This is a passive-agressive regression model used for continuous training. Find the notebook [here](https://www.kaggle.com/code/unofficialmerve/incremental-online-training-with-scikit-learn/) |
|
|
| ## Intended uses & limitations |
|
|
| This model is not ready to be used in production. It's trained to predict MPG a car spends based on it's attributes. |
|
|
| ## Training Procedure |
|
|
| ### Hyperparameters |
|
|
| The model is trained with below hyperparameters. |
|
|
| <details> |
| <summary> Click to expand </summary> |
|
|
| | Hyperparameter | Value | |
| |---------------------|---------------------| |
| | C | 0.01 | |
| | average | False | |
| | early_stopping | False | |
| | epsilon | 0.1 | |
| | fit_intercept | True | |
| | loss | epsilon_insensitive | |
| | max_iter | 1000 | |
| | n_iter_no_change | 5 | |
| | random_state | | |
| | shuffle | True | |
| | tol | 0.001 | |
| | validation_fraction | 0.1 | |
| | verbose | 0 | |
| | warm_start | False | |
|
|
| </details> |
|
|
| ### Model Plot |
|
|
| The model plot is below. |
|
|
| <style>#sk-1c3ea46c-0796-439d-856b-fedc4a20d47e {color: black;background-color: white;}#sk-1c3ea46c-0796-439d-856b-fedc4a20d47e pre{padding: 0;}#sk-1c3ea46c-0796-439d-856b-fedc4a20d47e div.sk-toggleable {background-color: white;}#sk-1c3ea46c-0796-439d-856b-fedc4a20d47e label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-1c3ea46c-0796-439d-856b-fedc4a20d47e label.sk-toggleable__label-arrow:before {content: "▸";float: left;margin-right: 0.25em;color: #696969;}#sk-1c3ea46c-0796-439d-856b-fedc4a20d47e label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-1c3ea46c-0796-439d-856b-fedc4a20d47e div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-1c3ea46c-0796-439d-856b-fedc4a20d47e div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-1c3ea46c-0796-439d-856b-fedc4a20d47e div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-1c3ea46c-0796-439d-856b-fedc4a20d47e input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-1c3ea46c-0796-439d-856b-fedc4a20d47e input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: "▾";}#sk-1c3ea46c-0796-439d-856b-fedc4a20d47e div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-1c3ea46c-0796-439d-856b-fedc4a20d47e div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-1c3ea46c-0796-439d-856b-fedc4a20d47e 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-1c3ea46c-0796-439d-856b-fedc4a20d47e 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-1c3ea46c-0796-439d-856b-fedc4a20d47e div.sk-estimator:hover {background-color: #d4ebff;}#sk-1c3ea46c-0796-439d-856b-fedc4a20d47e div.sk-parallel-item::after {content: "";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-1c3ea46c-0796-439d-856b-fedc4a20d47e div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-1c3ea46c-0796-439d-856b-fedc4a20d47e div.sk-serial::before {content: "";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 2em;bottom: 0;left: 50%;}#sk-1c3ea46c-0796-439d-856b-fedc4a20d47e div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;}#sk-1c3ea46c-0796-439d-856b-fedc4a20d47e div.sk-item {z-index: 1;}#sk-1c3ea46c-0796-439d-856b-fedc4a20d47e div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;}#sk-1c3ea46c-0796-439d-856b-fedc4a20d47e div.sk-parallel::before {content: "";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 2em;bottom: 0;left: 50%;}#sk-1c3ea46c-0796-439d-856b-fedc4a20d47e div.sk-parallel-item {display: flex;flex-direction: column;position: relative;background-color: white;}#sk-1c3ea46c-0796-439d-856b-fedc4a20d47e div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-1c3ea46c-0796-439d-856b-fedc4a20d47e div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-1c3ea46c-0796-439d-856b-fedc4a20d47e div.sk-parallel-item:only-child::after {width: 0;}#sk-1c3ea46c-0796-439d-856b-fedc4a20d47e 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-1c3ea46c-0796-439d-856b-fedc4a20d47e div.sk-label label {font-family: monospace;font-weight: bold;background-color: white;display: inline-block;line-height: 1.2em;}#sk-1c3ea46c-0796-439d-856b-fedc4a20d47e div.sk-label-container {position: relative;z-index: 2;text-align: center;}#sk-1c3ea46c-0796-439d-856b-fedc4a20d47e 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-1c3ea46c-0796-439d-856b-fedc4a20d47e div.sk-text-repr-fallback {display: none;}</style><div id="sk-1c3ea46c-0796-439d-856b-fedc4a20d47e" class="sk-top-container" style="overflow: auto;"><div class="sk-text-repr-fallback"><pre>PassiveAggressiveRegressor(C=0.01)</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="13f821ce-da7c-4825-b16d-1394a33b5711" type="checkbox" checked><label for="13f821ce-da7c-4825-b16d-1394a33b5711" class="sk-toggleable__label sk-toggleable__label-arrow">PassiveAggressiveRegressor</label><div class="sk-toggleable__content"><pre>PassiveAggressiveRegressor(C=0.01)</pre></div></div></div></div></div> |
| |
| ## Evaluation Results |
| |
| You can find the details about evaluation process and the evaluation results. |
| |
| |
| |
| | Metric | Value | |
| |----------|---------| |
| |
| # How to Get Started with the Model |
| |
| Use the code below to get started with the model. |
| |
| ```python |
| import joblib |
| import json |
| import pandas as pd |
| clf = joblib.load(skops47mqlzp0) |
| with open("config.json") as f: |
| config = json.load(f) |
| clf.predict(pd.DataFrame.from_dict(config["sklearn"]["example_input"])) |
| ``` |
| |
| |
| # 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] |
| ``` |
