The document discusses overfitting in machine learning models and how regularization helps address this problem. It explains that overfitting occurs when a model fits the training data too closely and fails to generalize to new data, resulting in large errors. Regularization helps reduce overfitting by penalizing model complexity, allowing the model to fit the training data while maintaining the ability to generalize to new observations. The document provides examples of different regularization techniques including ridge regression, lasso, and elastic net.

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Machine Learning A-Z
Regularization Intuition

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Machine Learning A-Z
The problem of Overfitting
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This fitting curve fits the data perfectly well

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Machine Learning A-Z
The problem of Overfitting
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But if we look at new observations, we can get large errors

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Machine Learning A-Z
The problem of Overfitting
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This is due to overfitting

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Machine Learning A-Z
Solution
Regularization

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Machine Learning A-Z
Examples of Regularization
Ridge Regression Lasso Elastic Net
Regularization

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Machine Learning A-Z
No Regularization

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Machine Learning A-Z
Ridge Regression

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Machine Learning A-Z
Lasso

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Machine Learning A-Z
Elastic Net

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Machine Learning A-Z
Regularization
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Regularization reduced overfitting