Machine learning and data mining 

Machinelearning venues 
Feature scaling is a method used to normalize the range of independent variables or features of data. In data processing, it is also known as data normalization and is generally performed during the data preprocessing step.
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✪ Why Do We Need to Perform Feature Scaling?

✪ Standardization Vs Normalization Feature Scaling

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✪ Why, How and When to Scale Features in Machine Learning?

✪ Feature Scaling Formula Quiz 1  Intro to Machine Learning
Transcription
Contents
Motivation
Since the range of values of raw data varies widely, in some machine learning algorithms, objective functions will not work properly without normalization. For example, many classifiers calculate the distance between two points by the Euclidean distance. If one of the features has a broad range of values, the distance will be governed by this particular feature. Therefore, the range of all features should be normalized so that each feature contributes approximately proportionately to the final distance.
Another reason why feature scaling is applied is that gradient descent converges much faster with feature scaling than without it.^{[1]}
Methods
Rescaling (minmax normalization)
Also known as minmax scaling or minmax normalization, is the simplest method and consists in rescaling the range of features to scale the range in [0, 1] or [−1, 1]. Selecting the target range depends on the nature of the data. The general formula for a minmax of [0, 1] is given as:
where is an original value, is the normalized value. For example, suppose that we have the students' weight data, and the students' weights span [160 pounds, 200 pounds]. To rescale this data, we first subtract 160 from each student's weight and divide the result by 40 (the difference between the maximum and minimum weights).
To rescale a range between an arbitrary set of values [a, b], the formula becomes:
where are the minmax values.
Mean normalization
where is an original value, is the normalized value. There is another form of the means normalization which is when we divide by the standard deviation which is also called standardization.
Standardization (Zscore Normalization)
In machine learning, we can handle various types of data, e.g. audio signals and pixel values for image data, and this data can include multiple dimensions. Feature standardization makes the values of each feature in the data have zeromean (when subtracting the mean in the numerator) and unitvariance. This method is widely used for normalization in many machine learning algorithms (e.g., support vector machines, logistic regression, and artificial neural networks).^{[2]}^{[citation needed]} The general method of calculation is to determine the distribution mean and standard deviation for each feature. Next we subtract the mean from each feature. Then we divide the values (mean is already subtracted) of each feature by its standard deviation.
Where is the original feature vector, is the mean of that feature vector, and is its standard deviation.
Scaling to unit length
Another option that is widely used in machinelearning is to scale the components of a feature vector such that the complete vector has length one. This usually means dividing each component by the Euclidean length of the vector:
In some applications (e.g., histogram features) it can be more practical to use the L_{1} norm (i.e., taxicab geometry) of the feature vector. This is especially important if in the following learning steps the scalar metric is used as a distance measure.^{[why?]}
Application
In stochastic gradient descent, feature scaling can sometimes improve the convergence speed of the algorithm^{[2]}^{[citation needed]}. In support vector machines,^{[3]} it can reduce the time to find support vectors. Note that feature scaling changes the SVM result^{[citation needed]}.
See also
 fMLLR, Feature space Maximum Likelihood Linear Regression
References
 ^ Ioffe, Sergey; Christian Szegedy (2015). "Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift". arXiv:1502.03167 [cs.LG].
 ^ ^{a} ^{b} Grus, Joel (2015). Data Science from Scratch. Sebastopol, CA: O'Reilly. pp. 99, 100. ISBN 9781491901427.
 ^ Juszczak, P.; D. M. J. Tax; R. P. W. Dui (2002). "Feature scaling in support vector data descriptions". Proc. 8th Annu. Conf. Adv. School Comput. Imaging: 25–30. CiteSeerX 10.1.1.100.2524.
Further reading
 Han, Jiawei; Kamber, Micheline; Pei, Jian (2011). "Data Transformation and Data Discretization". Data Mining: Concepts and Techniques. Elsevier. pp. 111–118.