independent of what the original values wereafter -scoringa value of zero is the mean and positive values are above the mean and negative values are below it now every feature is in the same unit (technicallyevery feature is now dimensionlessit has no unitsand we can mix dimensions more confidently in factif we now run our nearest neighbor classifierwe obtain percent accuracylook at the decision space again in two dimensionsit looks as shown in the following screenshotthe boundaries are now much more complex and there is interaction between the two dimensions in the full dataseteverything is happening in seven-dimensional space that is very hard to visualizebut the same principle applieswhere before few dimensions were dominantnow they are all given the same importance the nearest neighbor classifier is simplebut sometimes good enough we can generalize it to -nearest neighbor classifier by considering not just the closest point but the closest points all neighbors vote to select the label is typically small numbersuch as but can be largerparticularly if the dataset is very large