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→High variance и high bias
The variance is an error from sensitivity to small fluctuations in the training set. High variance can cause an algorithm to model the random noise in the training data, rather than the intended outputs (overfitting).
[[Файл:High_bias.jpg|border|500px300px|High variance и high bias]]
При использовании нейронных сетей variance увеличивается, а bias уменьшается с увеличением количества скрытых юнитов.
One way of resolving the trade-off is to use mixture models and ensemble learning.[13][14] For example, boosting combines many "weak" (high bias) models in an ensemble that has lower bias than the individual models, while bagging combines "strong" learners in a way that reduces their variance.
== Возможные решения ==
