The question we ask ourselves here is how many errors exist in the desired output from our machine learning algorithm. If we experience this, the learning algorithm may be attempting to fit the data too well, resulting in something we mentioned previously, over-fitting. Over-fitting can result from incorrect data, or a learning algorithm which is too complex for the task at hand. If this happens, we need to either tune our algorithm or look for one which will provide us with higher bias and lower variance.