10.28 Rescale Data in Rattle

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Different AI models require different characteristics of the data from which the model will be built. For example, when building a clustering using any kind of distance measure, we may need to normalise the data. Otherwise, a variable like income will overwhelm a variable like age, when calculating distances, considering that a distance of 10 ``years’’ may be more significant than a distance of $10,000 in income, yet, 10,000 swamps 10 when they are added together, as would be the case when calculating distances.

In these situations we will want to normalise our data. The types of normalisations (available through the Rescale feature of the Transform tab) we may want to perform include: re-centering and rescaling our data to be around zero (Recenter); rescaling our data to be in the range from 0 to 1 (Scale [0,1]); do a robust rescaling around zero using the median (-Median/MAD); taking the natural or base 10 logarithm of the number (Natural Log or Log 10); coverting the numbers into a rank ordering from 1 upwards (Rank); and rescaling into an interval from 0 upwards (Interval).

The approach Rattle takes to normalising (and to transforming) our data keeps the original data without modification. Instead, a new variable is created with a prefix added to the variable’s name that indicates the kind of transformation. Prefixes are, respectively, RRC_, R01_, RMD_, RLG_, R10_, RRK_, and RIN.

We can see the effect of the different normalisations in comparing the summary distributions in the second page of the display panel. It is also informative to compare the distributions visually through the Visual feature of the Explore tab where you can popout the plot for each of the new variables to compare side-by-side the impact of the transformations.

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