Standardization
Finally, let's start with the analysis of the PCA mathematical model.
First of all, we start by standardizing the data that the algorithm will work with. By standardization is meant the reduction of all continuous variables to a set where the mean will be equal to 0.
This is an important step because PCA cannot work properly if there is a variable in the dataset with a range of values 0-20 and 100-10,000, for example. PCA will start to "ignore" the characteristic with a small spread (0-20) and it will not be able to affect the result of the algorithm.
The formula for data standardization is very simple. Subtract the mean from the value of the variable and divide the result by the standard deviation:
The scikit-learn Python library allows us to do this in 1 line:
# Importing libraries
import numpy as np
from sklearn.preprocessing import StandardScaler
# Standardizing
X = np.asarray([[1, 3],[2, 10],[3, 35],[4, 40]], dtype = np.float64)
X_scaled = StandardScaler().fit_transform(X)
Swipe to start coding
Implement standardization of X array using the numpy functions np.mean() and np.std().
Soluzione
Grazie per i tuoi commenti!
single
Chieda ad AI
Chieda ad AI
Chieda pure quello che desidera o provi una delle domande suggerite per iniziare la nostra conversazione
Awesome!
Completion rate improved to 5.26Standardization
Scorri per mostrare il menu
Finally, let's start with the analysis of the PCA mathematical model.
First of all, we start by standardizing the data that the algorithm will work with. By standardization is meant the reduction of all continuous variables to a set where the mean will be equal to 0.
This is an important step because PCA cannot work properly if there is a variable in the dataset with a range of values 0-20 and 100-10,000, for example. PCA will start to "ignore" the characteristic with a small spread (0-20) and it will not be able to affect the result of the algorithm.
The formula for data standardization is very simple. Subtract the mean from the value of the variable and divide the result by the standard deviation:
The scikit-learn Python library allows us to do this in 1 line:
# Importing libraries
import numpy as np
from sklearn.preprocessing import StandardScaler
# Standardizing
X = np.asarray([[1, 3],[2, 10],[3, 35],[4, 40]], dtype = np.float64)
X_scaled = StandardScaler().fit_transform(X)
Swipe to start coding
Implement standardization of X array using the numpy functions np.mean() and np.std().
Soluzione
Cambia al desktop per esercitarti nel mondo realeContinua da dove ti trovi utilizzando una delle opzioni seguentiGrazie per i tuoi commenti!
Awesome!
Completion rate improved to 5.26single
