there are some caveats to supervised learning need to make sure that both your training and test datasets are large enough to actually be representative of your data you also need to make sure that you're catching all the different categories and outliers that you care aboutin both training and testingto get good measure of its successand to build good model you have to make sure that you've selected from those datasets randomlyand that you're not just carving your dataset in two and saying everything left of here is training and right here is testing you want to sample that randomlybecause there could be some pattern sequentially in your data that you don' know about nowif your model is overfittingand just going out of its way to accept outliers in your training datathen that' going to be revealed when you put it against unset scene of testing data this is because all that gyrations for outliers won' help with the outliers that it hasn' seen before let' be clear here that train/test is not perfectand it is possible to get misleading results from it maybe your sample sizes are too smalllike we already talked aboutor maybe just due to random chance your training data and your test data look remarkably similarthey actually do have similar set of outliers and you can still be overfitting as you can see in the following exampleit really can happen