--- library_name: sklearn tags: - sklearn - skops - tabular-regression model_file: linreg.pkl widget: structuredData: x0: - -0.3839236795902252 - -0.9788183569908142 - 1.0937178134918213 x1: - -0.5319488644599915 - -1.108436107635498 - 0.9354732036590576 x2: - -0.38279563188552856 - -1.3128694295883179 - 1.4773520231246948 x3: - 0.2815782427787781 - -0.11783809214830399 - -0.9529813528060913 x4: - 1.0 - 1.0 - 0.0 x5: - 0.0 - 0.0 - 0.0 x6: - 0.0 - 0.0 - 0.0 x7: - 0.0 - 0.0 - 1.0 x8: - 0.0 - 1.0 - 0.0 x9: - 0.0 - 0.0 - 0.0 --- # Model description This is a regression model on MPG dataset trained. ## Intended uses & limitations This model is not ready to be used in production. ## Training Procedure ### Hyperparameters The model is trained with below hyperparameters.
Click to expand | Hyperparameter | Value | |------------------|------------| | copy_X | True | | fit_intercept | True | | n_jobs | | | normalize | deprecated | | positive | False |
### Model Plot The model plot is below.
LinearRegression()
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
## Evaluation Results You can find the details about evaluation process and the evaluation results. | Metric | Value | |--------------------|----------| | Mean Squared Error | 5.01069 | | R-Squared | 0.883503 | # 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(linreg.pkl) 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] ```