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scikit-learn/
passive-agressive-regressor

Name: passive-agressive-regressor
Availability: InStock

sklearn

skops

tabular-regression

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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.

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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.

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

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Citation

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BibTeX:

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scikit-learn/passive-agressive-regressor