the only possibilities we have in this case is to either get more featuresmake the model more complexor change the model fixing high variance ifon the contrarywe suffer from high variance that means our model is too complex for the data in this casewe can only try to get more data or decrease the complexity this would mean to increase so that more neighbors would be taken into account or to remove some of the features high bias or low bias to find out what actually our problem iswe have to simply plot the train and test errors over the data size high bias is typically revealed by the test error decreasing bit at the beginningbut then settling at very high value with the train error approaching growing dataset size high variance is recognized by big gap between both curves plotting the errors for different dataset sizes for nn shows big gap between the train and test errorhinting at high variance problem refer to the following graph