Computer scienceData scienceMachine learningData preprocessing

Feature scaling

Normalizing the sample

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Suppose you have the following sample from the dataset:

[1, \, 0, \, -9, \, 5, \, 20]

Perform the row normalization (use the $L^2$ norm). The answer should be the sum of 2 values: the minimum and the maximum of the transformed sample, rounded up to the first decimal. During intermediary calculations, round the $L_2$ norm up to the first decimal, and then round the results of the sample division by the norm up to the first decimal.

As a reminder, to perform the row normalization, you have to calculate the $L_2$ norm of the sample:

\lVert x \rVert_{2} = \sqrt{ \sum_{i=1}^nx_i^2}

Then, each element in the sample is divided by the obtained norm.

The sample as a Python list

sample = [1, 0, -9, 5, 20]

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