In this video, learn what overfitting is and how it can result in a high-variance model.
- [Instructor] In this lesson we're going to talk about … another concept that we've briefly mentioned previously. … This problem is on the opposite extreme of underfitting … and not surprisingly this is called overfitting. … Once again, recall this plot and equation … from the last two lessons. … Total error is the sum of bias, variance … and some irreducible error that we can't control. … We learned in the last lesson … that the left side of this plot represents underfitting. … On the other extreme the right side … of this plot represents overfitting. … So we're looking at a very complex model … where we have low bias, but high variance, … which drives high total error. … Revisiting prior definitions, variance refers … to an algorithm's sensitivity to small fluctuations … in the training data. … High variance is a result of an algorithm fitting … to random noise in the training data. … And overfitting occurs when an algorithm … fits too closely to a limited set of data. … In other words, the model might just memorize the examples …
Author
Released
5/10/2019- What is machine learning (ML)?
- ML vs. deep learning vs. AI
- Handling common challenges in ML
- Plotting continuous features
- Continuous and categorical data cleaning
- Measuring success
- Overfitting and underfitting
- Tuning hyperparameters
- Evaluating a model
Skill Level Beginner
Duration
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Why do we split up our data?5m 54s
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What is underfitting?2m 26s
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What is overfitting?2m 47s
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Finding the optimal tradeoff3m 16s
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Hyperparameter tuning6m 22s
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Regularization2m 31s
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Conclusion
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Next steps1m 23s
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Video: What is overfitting?