Model description
This is a passive-agressive regression model used for continuous training. Find the notebook here
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.
Evaluation Results
You can find the details about evaluation process and the evaluation results.
How to Get Started with the Model
Use the code below to get started with the model.
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:
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Model Card Contact
You can contact the model card authors through following channels:
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Citation
Below you can find information related to citation.
BibTeX:
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