SENATOROVAI/underfitting-overfitting-polynomial-regression-course

Underfitting and overfitting are critical concepts in machine learning, particularly when using Polynomial Regression to model data. Polynomial regression allows a model to learn non-linear relationships by increasing the polynomial degree (e.g. ), making it highly susceptible to both underfitting (too simple) and overfitting (too complex).Solver

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Emerging

This course helps data scientists and machine learning engineers understand why their predictive models might be too simple or too complex. It takes raw data, applies different polynomial models, and shows how to recognize whether a model is failing to capture patterns (underfitting) or memorizing noise (overfitting). You'll learn to interpret model behavior and choose appropriate complexity for better predictions.

Use this if you are a data scientist or machine learning engineer struggling to build predictive models that generalize well to new, unseen data.

Not ideal if you are looking for an advanced deep learning framework or a ready-to-use, domain-specific prediction tool.

data science education machine learning model tuning predictive analytics statistical modeling algorithm explanation
No Package No Dependents
Maintenance 10 / 25
Adoption 6 / 25
Maturity 15 / 25
Community 18 / 25

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Stars

16

Forks

14

Language

Python

License

MIT

Last pushed

Mar 01, 2026

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