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Comment
@bopon4090 I'm not sure I said high bias = low variance, but there tends to be a trade off between the two. In the area of Machine Learning, "bias" tends to refer to how well your method (linear regression, decision trees, neural networks etc) can be fit to the training data. If the training data are fit perfectly, then we say Bias = 0, and if the data are not fit perfectly, then Bias > 0. However, fitting the training data perfectly does not always mean we will do well with future datasets, and variance is a measure of how well the model performs with future datasets.
youtube AI Bias 2021-04-14T17:0…
Coding Result
DimensionValue
Responsibilitynone
Reasoningunclear
Policynone
Emotionindifference
Coded at2026-04-27T06:26:44.938723
Raw LLM Response
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