Notice: This page requires JavaScript to function properly.
Please enable JavaScript in your browser settings or update your browser.
Impara StandardScaler | Scaling Numerical Data
Introduction to Scikit Learn

bookStandardScaler

If the dataset is standardized, it will have a good optimization effect for many machine learning algorithms. To get standardized data you have to use the next formula:

Here we have the following values:

  • x_scaled - standardized feature element,
  • x - unnormalized feature element,
  • mean - mean value,
  • std - standard deviation value.

There is a function in the sklearn library that normalizes data according to the formula given above: MaxAbsScaler(). In order to work with this function, it must first be imported in such a way:

The main property of standardized data is that this data: mean = 0 and standard deviation = 1.

1
from sklearn.preprocessing import StandarsScaler
copy

This function works like the previous two, namely MinMaxScaler, MaxAbsScaler, and it works in a similar way. So, in this chapter there is no example of using StandartScaler function. You will use it on your own in the below task.

Let's try! If you have some difficulties, please, use hints.

Compito

Swipe to start coding

You have wine dataset, we have worked with it recently. Please, standardize this data. To check, if StandardScaler function works correct, please dispay the mean and standard deviation. Pay attention: mean will be equal to 0 and std to 1.

Soluzione

Tutto è chiaro?

Come possiamo migliorarlo?

Grazie per i tuoi commenti!

Sezione 2. Capitolo 3
single

single

Chieda ad AI

expand

Chieda ad AI

ChatGPT

Chieda pure quello che desidera o provi una delle domande suggerite per iniziare la nostra conversazione

close

Awesome!

Completion rate improved to 12.5

bookStandardScaler

Scorri per mostrare il menu

If the dataset is standardized, it will have a good optimization effect for many machine learning algorithms. To get standardized data you have to use the next formula:

Here we have the following values:

  • x_scaled - standardized feature element,
  • x - unnormalized feature element,
  • mean - mean value,
  • std - standard deviation value.

There is a function in the sklearn library that normalizes data according to the formula given above: MaxAbsScaler(). In order to work with this function, it must first be imported in such a way:

The main property of standardized data is that this data: mean = 0 and standard deviation = 1.

1
from sklearn.preprocessing import StandarsScaler
copy

This function works like the previous two, namely MinMaxScaler, MaxAbsScaler, and it works in a similar way. So, in this chapter there is no example of using StandartScaler function. You will use it on your own in the below task.

Let's try! If you have some difficulties, please, use hints.

Compito

Swipe to start coding

You have wine dataset, we have worked with it recently. Please, standardize this data. To check, if StandardScaler function works correct, please dispay the mean and standard deviation. Pay attention: mean will be equal to 0 and std to 1.

Soluzione

Switch to desktopCambia al desktop per esercitarti nel mondo realeContinua da dove ti trovi utilizzando una delle opzioni seguenti
Tutto è chiaro?

Come possiamo migliorarlo?

Grazie per i tuoi commenti!

close

Awesome!

Completion rate improved to 12.5
Sezione 2. Capitolo 3
single

single

some-alt