Can anyone explain me StandardScaler?

问题

I am unable to understand the page of the StandardScaler in the documentation of sklearn.

Can anyone explain this to me in simple terms?

回答1:

The idea behind StandardScaler is that it will transform your data such that its distribution will have a mean value 0 and standard deviation of 1.
In case of multivariate data, this is done feature-wise (in other words independently for each column of the data).
Given the distribution of the data, each value in the dataset will have the mean value subtracted, and then divided by the standard deviation of the whole dataset (or feature in the multivariate case).

回答2:

The main idea is to normalize/standardize (mean = 0 and standard deviation = 1) your features/variables/columns of X before applying machine learning techniques.

One important thing that you should keep in mind is that most (if not all) scikit-learn models/classes/functions, expect as input a matrix X with dimensions/shape [number_of_samples, number_of_features]. This is very important. Some other libraries expect as input the inverse.

IMPORTNANT: StandardScaler() will normalize the features (each column of X, INDIVIDUALLY !!!) so that each column/feature/variable will have mean = 0 and standard deviation = 1.

P.S: I find the most upvoted answer on this page, wrong. I am quoting "each value in the dataset will have the sample mean value subtracted" -- This is not true either correct.

Example:

from sklearn.preprocessing import StandardScaler
import numpy as np

# 4 samples/observations and 2 variables/features
data = np.array([[0, 0], [1, 0], [0, 1], [1, 1]])
scaler = StandardScaler()
scaled_data = scaler.fit_transform(data)

print(data)
[[0, 0],
 [1, 0],
 [0, 1],
 [1, 1]])

print(scaled_data)
[[-1. -1.]
 [ 1. -1.]
 [-1.  1.]
 [ 1.  1.]]

Verify that the mean of each feature (column) is 0:

scaled_data.mean(axis = 0)
array([0., 0.])

Verify that the std of each feature (column) is 1:

scaled_data.std(axis = 0)
array([1., 1.])

The maths:

UPDATE 08/2019: Concering the input parameters with_mean and with_std to False/True, I have provided an answer here: https://stackoverflow.com/a/57381708/5025009

回答3:

How to calculate it:

You can read more here:

http://sebastianraschka.com/Articles/2014_about_feature_scaling.html#standardization-and-min-max-scaling

回答4:

StandardScaler performs the task of Standardization. Usually a dataset contains variables that are different in scale. For e.g. an Employee dataset will contain AGE column with values on scale 20-70 and SALARY column with values on scale 10000-80000.
As these two columns are different in scale, they are Standardized to have common scale while building machine learning model.

回答5:

This is useful when you want to compare data that correspond to different units. In that case, you want to remove the units. To do that in a consistent way of all the data, you transform the data in a way that the variance is unitary and that the mean of the series is 0.

回答6:

The answers above are great, but I needed a simple example to alleviate some concerns that I have had in the past. I wanted to make sure it was indeed treating each column separately. I am now reassured and can't find what example had caused me concern. All columns ARE scaled separately as described by those above.

CODE

import pandas as pd
import scipy.stats as ss
from sklearn.preprocessing import StandardScaler


data= [[1, 1, 1, 1, 1],[2, 5, 10, 50, 100],[3, 10, 20, 150, 200],[4, 15, 40, 200, 300]]

df = pd.DataFrame(data, columns=['N0', 'N1', 'N2', 'N3', 'N4']).astype('float64')

sc_X = StandardScaler()
df = sc_X.fit_transform(df)

num_cols = len(df[0,:])
for i in range(num_cols):
    col = df[:,i]
    col_stats = ss.describe(col)
    print(col_stats)

OUTPUT

DescribeResult(nobs=4, minmax=(-1.3416407864998738, 1.3416407864998738), mean=0.0, variance=1.3333333333333333, skewness=0.0, kurtosis=-1.3599999999999999)
DescribeResult(nobs=4, minmax=(-1.2828087129930659, 1.3778315806221817), mean=-5.551115123125783e-17, variance=1.3333333333333337, skewness=0.11003776770595125, kurtosis=-1.394993095506219)
DescribeResult(nobs=4, minmax=(-1.155344148338584, 1.53471088361394), mean=0.0, variance=1.3333333333333333, skewness=0.48089217736510326, kurtosis=-1.1471008824318165)
DescribeResult(nobs=4, minmax=(-1.2604572012883055, 1.2668071116222517), mean=-5.551115123125783e-17, variance=1.3333333333333333, skewness=0.0056842140599118185, kurtosis=-1.6438177182479734)
DescribeResult(nobs=4, minmax=(-1.338945389819976, 1.3434309690153527), mean=5.551115123125783e-17, variance=1.3333333333333333, skewness=0.005374558840039456, kurtosis=-1.3619131970819205)

回答7:

Following is a simple working example to explain how standarization calculation works. The theory part is already well explained in other answers.

>>>import numpy as np
>>>data = [[6, 2], [4, 2], [6, 4], [8, 2]]
>>>a = np.array(data)

>>>np.std(a, axis=0)
array([1.41421356, 0.8660254 ])

>>>np.mean(a, axis=0)
array([6. , 2.5])

>>>from sklearn.preprocessing import StandardScaler
>>>scaler = StandardScaler()
>>>scaler.fit(data)
>>>print(scaler.mean_)

#Xchanged = (X−μ)/σ  WHERE σ is Standard Deviation and μ is mean
>>>z=scaler.transform(data)
>>>z

Calculation

As you can see in the output, mean is [6. , 2.5] and std deviation is [1.41421356, 0.8660254 ]

Data is (0,1) position is 2 Standardization = (2 - 2.5)/0.8660254 = -0.57735027

Data in (1,0) position is 4 Standardization = (4-6)/1.41421356 = -1.414

Result After Standardization

Check Mean and Std Deviation After Standardization

Note: -2.77555756e-17 is very close to 0.

References