Understanding Overfitting in Machine Learning
Learn about overfitting in machine learning, how to detect it, prevent it, and the role of regularization in reducing generalization error. Explore techniques to avoid overfitting and improve model performance through proper training and testing sets.
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Presentation Transcript
Regularization in Machine Learning Tyrone Rees SCD Computational Mathematics
What is overfitting? https://medium.com/greyatom/what-is-underfitting-and-overfitting-in-machine-learning-and-how-to-deal-with-it-6803a989c76
What is overfitting? https://medium.com/greyatom/what-is-underfitting-and-overfitting-in-machine-learning-and-how-to-deal-with-it-6803a989c76
What is overfitting? https://medium.com/greyatom/what-is-underfitting-and-overfitting-in-machine-learning-and-how-to-deal-with-it-6803a989c76
Test and trial sets All data Training set Test set https://gerardnico.com/data_mining/overfitting
https://hackernoon.com/memorizing-is-not-learning-6-tricks-to-prevent-overfitting-in-machine-learning-820b091dc42https://hackernoon.com/memorizing-is-not-learning-6-tricks-to-prevent-overfitting-in-machine-learning-820b091dc42
How can we avoid overfitting? https://www.amazon.com/Munch-Gifts-Machine-Learning-Overfitted/dp/B07G6YJ8QH
Regularization Regularization is any modification we make to a learning algorithm that is intended to reduce its generalization error but not its training error. Ian Goodfellow (Google)
