Bias is a measure to see how a model learns, and variance is the amount of variation that the target function can have when supplied with new training data. In simple terms, bias is a measure of how far the predicted values are from the actual values. It is considered to be high if the values are far off. Variance is a situation where the model performs well on the trained dataset but does not perform well on a dataset that has not trained yet. This could be a different dataset from the testing pool or the validation pool. All in all, variance is a measure of how scattered the values are when the predicted and the actual values are juxtaposed. High variance implies overfitting and that the model is picking up noise present in the training dataset.
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