Course Content
Introduction to Scikit Learn
Introduction to Scikit Learn
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:
from sklearn.preprocessing import MaxAbsScaler
Let's look at an example of how we apply this normalization to a very simple array.
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]
[object Object]It's time to practice!
Task
You have a numpy array. Please, normalize this array into range [-1, 1].
Switch to desktop for real-world practiceContinue from where you are using one of the options belowThanks for your feedback!
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:
from sklearn.preprocessing import MaxAbsScaler
Let's look at an example of how we apply this normalization to a very simple array.
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]
[object Object]It's time to practice!
Task
You have a numpy array. Please, normalize this array into range [-1, 1].
Switch to desktop for real-world practiceContinue from where you are using one of the options belowThanks for your feedback!
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:
from sklearn.preprocessing import MaxAbsScaler
Let's look at an example of how we apply this normalization to a very simple array.
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]
[object Object]It's time to practice!
Task
You have a numpy array. Please, normalize this array into range [-1, 1].
Switch to desktop for real-world practiceContinue from where you are using one of the options belowThanks for your feedback!
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:
from sklearn.preprocessing import MaxAbsScaler
Let's look at an example of how we apply this normalization to a very simple array.
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]
[object Object]It's time to practice!
Task
You have a numpy array. Please, normalize this array into range [-1, 1].
Switch to desktop for real-world practiceContinue from where you are using one of the options belowSwitch to desktop for real-world practiceContinue from where you are using one of the options below