Overfitting and Underfitting | Bias and Variance Tradeoff in Machine Learning | Clearly Explained!
๐ฅ Overfitting and Underfitting are two major problems that can be encountered during machine learning model training. Overfitting occurs when your model is more complex than you need and captures the noise of the training data, which is unique to train data only. Meaning, it does not apply to validation, test or other data in the domain. Underfitting happens when your model is to weak to find enough patterns and fails to provide nice results even for the training data.
Bias and variance tradeoff is the main concept behind it. You should always control the complexity of your model, as well asโฆ
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Chapters (7)
Introduction.
0:14
Underfitting.
0:30
Overfitting.
0:53
Two different scenarios for overfitting.
1:19
Best case.
1:29
Bias and variance tradeoff.
2:03
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