different kinds of preprocessing the first plot in figure - shows synthetic two-class classification dataset with two features the first feature (the -axis valueis between and the second feature (the -axis valueis between around and the following four plots show four different ways to transform the data that yield more standard ranges the standardscaler in scikit-learn ensures that for each feature the mean is and the variance is bringing all features to the same magnitude howeverthis scaling does not ensure any particular minimum and maximum values for the features the robustscaler works similarly to the standardscaler in that it ensures statistical properties for each feature that guarantee that they are on the same scale howeverthe robustscaler uses the median and quartiles, instead of mean and variance this makes the robustscaler ignore data points that are very different from the rest (like measurement errorsthese odd data points are also called outliersand can lead to trouble for other scaling techniques the minmaxscaleron the other handshifts the data such that all features are exactly between and for the two-dimensional dataset this means all of the data is con the median of set of numbers is the number such that half of the numbers are smaller than and half of the numbers are larger than the lower quartile is the number such that one-fourth of the numbers are smaller than xand the upper quartile is the number such that one-fourth of the numbers are larger than preprocessing and scaling