What is the consequence of a model having low bias and high variance? Overfitting Underfitting High generalization Low computational complexity
Question
Solution 1
The consequence of a model having low bias and high variance is Overfitting.
Here's why:
Bias refers to the error due to the model's simplistic assumptions in learning from the data. Low bias means the model learns too well from the data, capturing the complex patterns within it.
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