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

StandardScaler

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:

[object Object]

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

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.

Tarefa

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.

Tarefa

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

Tudo estava claro?

Seção 2. Capítulo 3

StandardScaler

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:

[object Object]

Here we have the following values:

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

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


              1
            
from sklearn.preprocessing import StandarsScaler

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

Tarefa

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

Tudo estava claro?

Seção 2. Capítulo 3

StandardScaler

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:

[object Object]

Here we have the following values:

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

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


              1
            
from sklearn.preprocessing import StandarsScaler

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

Tarefa

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

Tudo estava claro?

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:

[object Object]

Here we have the following values:

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

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


              1
            
from sklearn.preprocessing import StandarsScaler

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

Tarefa

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

Seção 2. Capítulo 3

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