Standardization

Finally, let's start with the analysis of the PCA mathematical model.

First of all, we start by standardizing the data that the algorithm will work with. By standardization is meant the reduction of all continuous variables to a set where the mean will be equal to 0.

This is an important step because PCA cannot work properly if there is a variable in the dataset with a range of values 0-20 and 100-10,000, for example. PCA will start to "ignore" the characteristic with a small spread (0-20) and it will not be able to affect the result of the algorithm.

The formula for data standardization is very simple. Subtract the mean from the value of the variable and divide the result by the standard deviation:

The scikit-learn Python library allows us to do this in 1 line:

python

Opgave

Swipe to start coding

Implement standardization of X array using the numpy functions np.mean() and np.std().

Løsning

Skift til skrivebord for at øve i den virkelige verdenFortsæt der, hvor du er, med en af nedenstående muligheder

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Sektion 2. Kapitel 1

Standardization

Finally, let's start with the analysis of the PCA mathematical model.

First of all, we start by standardizing the data that the algorithm will work with. By standardization is meant the reduction of all continuous variables to a set where the mean will be equal to 0.

The formula for data standardization is very simple. Subtract the mean from the value of the variable and divide the result by the standard deviation:

The scikit-learn Python library allows us to do this in 1 line:

python

Opgave

Swipe to start coding

Implement standardization of X array using the numpy functions np.mean() and np.std().

Løsning

Skift til skrivebord for at øve i den virkelige verdenFortsæt der, hvor du er, med en af nedenstående muligheder

Var alt klart?

Tak for dine kommentarer!

Sektion 2. Kapitel 1

Skift til skrivebord for at øve i den virkelige verdenFortsæt der, hvor du er, med en af nedenstående muligheder