Scaling_synthesized_data

In particular, I checked out the k-Nearest Neighbors (k-NN) and logistic regression algorithms and saw how scaling numerical data strongly influenced the performance of the former but not that of the latter, as measured, for example, by accuracy (see Glossary below or previous articles for definitions of scaling, k-NN and other relevant terms). The real take home message here was that preprocessing doesn't occur in a vacuum, that is, you can prepocess the heck out of your data but the proof is in the pudding: how well does your model then perform? Scaling numerical data (that is, multiplying all instances of a variable by a constant in order to change that variable's range) has two related purposes: i) if your measurements are in meters and mine are in miles, then, if we both scale our data, they end up being the same & ii) if two variables have vastly different ranges, the one with the larger range may dominate your predictive model, even though it may be less important to your target variable than the variable with the smaller range. What we saw is that this problem identified in ii) occurs with k-NN, which explicitly looks at how close data are to one another but not in logistic regression which, when being trained, will shrink the relevant coefficient to account for the lack of scaling. As the data we used in the previous articles was real-world data, all we could see was how the models performed before and after scaling.