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---
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]
``` |