Deep Learning
Having high bias means the model is too simple and can’t capture many features during the training phase. This is also known as overfitting.
False
Proposed by @svpino
Card 1 of 259. Answer: False
False
Proposed by @svpino
Card 1 of 259. Answer: False
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False. The first part is roughly right: high bias means the model is too simple to capture the real pattern in the data. But that is underfitting, not overfitting. Bias is the error that comes from wrong or overly restrictive assumptions, such as fitting a straight line to a curved relationship.
The bias-variance decomposition splits expected squared error into bias² + variance + irreducible noise. A high-bias model does poorly everywhere: say 70% training accuracy and 68% validation accuracy. An overfit, high-variance model memorizes the training data, noise included: say 99% training accuracy but 75% on validation. A large gap between the two signals variance; poor scores on both signal bias.
The usual follow-up is how to fix each one. For high bias, use a more flexible model, add better features, reduce regularization or train longer; more data rarely helps. For high variance, get more data, add regularization such as weight decay or dropout, stop early or simplify the model.