Chain of Assumptions in Supervised Statistical Learning
For a supervised learning algorithm to do well, you need to check the following four assumptions:
- Low Bias: Fits training set well on cost function (e.g., comparable to human level performance)
- Low Variance: Fits dev set on cost function
- Fits test set well on cost function
- Performs well in real world (e.g., happy users)
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