Why enlarging a model can lower bias
When a system is underfitting, adding capacity can help it learn the training patterns more accurately. In a neural network, this usually means using more layers, more hidden units, or both. A larger model may also increase variance, so techniques such as weight decay, dropout, or early stopping are often used to keep that side effect under control.
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