1- When the training set is small, the contribution of variance to error may be more...
1- When the training set is small, the contribution of variance to error may be more than that of bias and in such a case, we may prefer a simple model even though we know that it is too simple for the task. In your own words, explain why this is the case.
2-For small training sets variance may contribute more to the overall error than bias. Sometimes this is handled by reducing the complexity of the model, even if the model is too simple. Why do you suppose this is the case? Come up with your own example of this.
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1- When the training set is small, the contribution of variance
to error may be more...
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