---
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.
Click to expand
| 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 |
### Model Plot
The model plot is below.
PassiveAggressiveRegressor(C=0.01)
Please rerun this cell to show the HTML repr or trust the notebook.
PassiveAggressiveRegressor(C=0.01)
## 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]
```