Conteúdo do Curso

Introduction to Scikit Learn

1. The Very First Steps

Loading Data Sets SimpleImputer Ordinal Encoding

2. Scaling Numerical Data

MinMaxScaler MaxAbsScaler StandardScaler

3. Models in Scikit Learn

Prepearing Data Set 1/2 Prepearing Data Set 2/2

MaxAbsScaler

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

[object Object]

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:


              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)

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

[object Object]

It's time to practice!

Tarefa

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

Tarefa

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

Mude para o desktop para praticar no mundo realContinue de onde você está usando uma das opções abaixo

Tudo estava claro?

Seção 2. Capítulo 2

MaxAbsScaler

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

[object Object]

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)

[object Object]

It's time to practice!

Tarefa

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

Tarefa

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

Mude para o desktop para praticar no mundo realContinue de onde você está usando uma das opções abaixo

Tudo estava claro?

Seção 2. Capítulo 2

MaxAbsScaler

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

[object Object]

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)

[object Object]

It's time to practice!

Tarefa

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

Tarefa

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

Mude para o desktop para praticar no mundo realContinue de onde você está usando uma das opções abaixo

Tudo estava claro?

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

[object Object]

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)

[object Object]

It's time to practice!

Tarefa

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

Mude para o desktop para praticar no mundo realContinue de onde você está usando uma das opções abaixo

Seção 2. Capítulo 2

Mude para o desktop para praticar no mundo realContinue de onde você está usando uma das opções abaixo