Feature Scaling

What is Feature Scaling?

Feature Scaling is an important pre-processing step for some machine learning algorithms.

Imagine you have three friends of whom you know the individual weight and height.

You would like to deduce Christian’s  t-shirt size from David’s and Julia’s by looking at the height and weight.

Name Height in m Weight in kg T-Shirt size
Julia 1.58 52 Small
David 1.79 79 Large
Christian 1.86 64 ?

One way You could determine the shirt size is to just add up the weight and the height of each friend. You would get: Continue reading “Feature Scaling”

Receiver Operating Characteristic

ROC Curve

As we already introduced Precision and Recall  the ROC curve is another way of looking at the quality of classification algorithms.

ROC stands for Receiver Operating Characteristic

The ROC curve is created by plotting the true positive rate (TPR) on the y-axis against the false positive rate (FPR) on the x-axis at various threshold settings.

You already know the TPR as recall or sensitivity.

Continue reading “Receiver Operating Characteristic”

Classification: Precision and Recall

In the realms of Data Science you’ll encounter sooner or the later the terms “Precision” and “Recall”. But what do they mean?


Living together with little kids You very often run into classification issues:

My daughter really likes dogs, so seeing a dog is something positive. When she sees a normal dog e.g. a Labrador and proclaims: “Look, there is a dog!”

That’s a True Positive (TP) Continue reading “Classification: Precision and Recall”