Зміст курсу
Introduction to Scikit Learn
Introduction to Scikit Learn
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.
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.
Завдання
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.
Перейдіть на комп'ютер для реальної практикиПродовжуйте з того місця, де ви зупинились, використовуючи один з наведених нижче варіантівДякуємо за ваш відгук!
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.
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.
Завдання
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.
Перейдіть на комп'ютер для реальної практикиПродовжуйте з того місця, де ви зупинились, використовуючи один з наведених нижче варіантівДякуємо за ваш відгук!
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.
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.
Завдання
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.
Перейдіть на комп'ютер для реальної практикиПродовжуйте з того місця, де ви зупинились, використовуючи один з наведених нижче варіантівДякуємо за ваш відгук!
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.
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.
Завдання
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.
Перейдіть на комп'ютер для реальної практикиПродовжуйте з того місця, де ви зупинились, використовуючи один з наведених нижче варіантівПерейдіть на комп'ютер для реальної практикиПродовжуйте з того місця, де ви зупинились, використовуючи один з наведених нижче варіантів