I always read in books that when we do classification or machine learning tasks it's always better to normalize the features so to make them in one range like 0-1. Today I used weka to play with Iris dataset. First I just built a J48 classifier without normalizing the values, and the it made perfect performance. However when I normalized all the features to be in the range 0-1, the classifier made so much mistakes. Why is that? Shouldn't normalization be used always?
Solved – Is normalizing the features always good for classification
classificationdata miningnormalization
Best Answer
It depends on the algorithm. For some algorithms normalization has no effect. Generally, algorithms that work with distances tend to work better on normalized data but this doesn't mean the performance will always be higher after normalization.
Note that many algorithms have tuning parameters which you may need to change after normalization. What you are seeing may just be that the default parameter settings for J48 happened to work well for the unnormalized data.