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Striking a Balance with Regularization Techniques

Striking a Balance with Regularization Techniques - SQL教程

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Striking a Balance with Regularization Techniques

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- [Instructor] Welcome back to your adventure into machine learning. Now that you've visited the art of capturing complex relationships with polynomial and interaction terms, this video introduces two powerful allies in your quest: lasso and ridge regression, kind of like adding safety gear to your skateboarding toolkit. In the world of skateboarding, overcomplicating a trick with unnecessary flips and spins might not only miss the mark, but also lead to a spectacular wipe out. Similarly, a regression model that's too complex risks overfitting your data, performing well on your current dataset, but failing to generalize to new or unseen data. Overfitting is like a skater who's only mastered a specific ramp in their local park, but struggles with any new terrain. Lasso regression helps us avoid overfitting by applying a penalty to the absolute size of the regression coefficients. Think of it as a skater who streamlines their tricks, focusing on precision and finesse rather than…

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