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MaxAbsScaler

To bring values into range [-1, 1] we have to use the next formula:

Here we have the following values:

x_scaled - normalized feature element,
x - unnormalized feature element,
max(x) -- maximum feature element.

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:


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from sklearn.preprocessing import MaxAbsScaler

Let's look at an example of how we apply this normalization to a very simple array.


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from sklearn.preprocessing import MaxAbsScaler
data = [[10, 5, -6],[11, -9, 4],[-10, 0, 1]]

# Normalizer initialization
scaler = MaxAbsScaler()
# Dataset transfer and transformation
scaler.fit(data)
scaled_data = scaler.transform(data)
print('Data before normalization', data)
print('Data after normalization', scaled_data)

If you run this code you will get two different arrays: before and after normalization. And this function really works, because you can make sure that data after using MaxAbsScaler() function really lie within an interval [-1, 1]. Look below.v

It's time to practice!

Tâche

Swipe to start coding

You have a numpy array. Please, normalize this array into range [-1, 1].

Solution

Passez à un bureau pour une pratique réelleContinuez d'où vous êtes en utilisant l'une des options ci-dessous

Tout était clair ?

Merci pour vos commentaires !

Section 2. Chapitre 2

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MaxAbsScaler

To bring values into range [-1, 1] we have to use the next formula:

Here we have the following values:

x_scaled - normalized feature element,
x - unnormalized feature element,
max(x) -- maximum feature element.


              1
            
from sklearn.preprocessing import MaxAbsScaler

Let's look at an example of how we apply this normalization to a very simple array.


              12345678910
            
from sklearn.preprocessing import MaxAbsScaler
data = [[10, 5, -6],[11, -9, 4],[-10, 0, 1]]

# Normalizer initialization
scaler = MaxAbsScaler()
# Dataset transfer and transformation
scaler.fit(data)
scaled_data = scaler.transform(data)
print('Data before normalization', data)
print('Data after normalization', scaled_data)

It's time to practice!

Tâche

Swipe to start coding

You have a numpy array. Please, normalize this array into range [-1, 1].

Solution

Passez à un bureau pour une pratique réelleContinuez d'où vous êtes en utilisant l'une des options ci-dessous

Tout était clair ?

Merci pour vos commentaires !

Section 2. Chapitre 2

Passez à un bureau pour une pratique réelleContinuez d'où vous êtes en utilisant l'une des options ci-dessous