What is the difference between bias and variance and how does it affect machine learning outcomes?

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Anonymous · Accepted Answer

Bias means underfitting. The model is too simple that it does not learn all the
patterns in the training data. When using it in the testing dataset, it will
miss several patterns. Variance means overfitting. The model learns even noise
data, causing it to be sensitive to outliers during testing. The general pattern
is impacted by noise in testing data.

What is the difference between bias and variance and how does it affect machine learning outcomes?

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