In the realm of machine learning, what exactly is the bias-variance tradeoff, and can you describe an instance where you have seen this tradeoff manifest in a model's performance?

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Anonymous · Accepted Answer

bias is the average error of the model and variance is the variance of the model
prediction. A model with high bias and low variance have predictions mapped into
the same place in the space, a model with high variance and low bias has
scattered predictions. The first one suggests that the model is too simple, it
has high training error and testing error, the second one suggests overfitting
when the model has low training error but high test error. I have seen this
happen when a deep learning model starts overfitting, in that case I have used
techniques to mitigate it like early stopping, regularization and dropout
layer.

In the realm of machine learning, what exactly is the bias-variance tradeoff, and can you describe an instance where you have seen this tradeoff manifest in a model's performance?

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