- Get more data for better generalization on unseen data.
- Regularization (L2 Regularization, Dropout, Data Augmentation, etc.). 
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University of Michigan - Ann Arbor

Bias and Variance are two fundamental concepts for Machine Learning, and their intuition is just a little different from what you might have learned in your statistics class. Here the video go through two examples that make these concepts super easy to understand.


https://www.youtube.com/watch?app=desktop&v=EuBBz3bI-aA

Bias and Variance

Train/dev set error and bias/variance

When the number of training samples tends to infinity, the changing trend of the model trained on the data set:

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