# Feature Scaling and its importance in data Preprocessing Normalization vs Standardization.

Feature scaling refers to the methods or techniques used to normalize the range of independent variables in our data, or in other words, the methods to set the feature value range within a similar scale. Feature scaling is generally the last step in the data preprocessing pipeline, performed just before training the machine learning algorithms.

## Feature Scaling

Feature scaling refers to the methods or techniques used to normalize the range of independent variables in our data, or in other words, the methods to set the feature value range within a similar scale. Feature scaling is generally the last step in the data preprocessing pipeline, performed just before training the machine learning algorithms.

### Feature magnitude matters because:

• The regression coefficients of linear models are directly influenced by the scale of the variable.
• Variables with bigger magnitude / larger value range dominate over those with smaller magnitude / value range.
• Gradient descent converges faster when features are on similar scales.
• Feature scaling helps decrease the time to find support vectors for SVMs
• Euclidean distances are sensitive to feature magnitude.
• Some algorithms, like PCA require the features to be centered at 0.

### The machine learning models affected by the feature scaling are:

• Linear and Logistic Regression
• Neural Networks
• Support Vector Machines
• KNN
• K-means clustering
• Linear Discriminant Analysis (LDA)
• Principal Component Analysis (PCA)

### There are several Feature Scaling techniques like

• Standardisation
• Mean normalisation
• Scaling to minimum and maximum values — MinMaxScaling
• Scaling to maximum value — MaxAbsScaling
• Scaling to quantiles and median — RobustScaling
• Normalization to vector unit length

but here I will talk about the importance of Standardisation and Normalization.

### Standardisation

Standardisation involves centering the variable at zero, and standardising the variance to 1. The procedure involves subtracting the mean of each observation and then dividing by the standard deviation:

z = (x — x_mean) / std

The result of the above transformation is z, which is called the z-score, and represents how many standard deviations a given observation deviates from the mean. A z-score specifies the location of the observation within a distribution (in numbers of standard deviations respect to the mean of the distribution). The sign of the z-score (+ or — ) indicates whether the observation is above (+) or below ( — ) the mean.

The shape of a standardised (or z-scored normalised) distribution will be identical to the original distribution of the variable. If the original distribution is normal, then the standardised distribution will be normal. But, if the original distribution is skewed, then the standardised distribution of the variable will also be skewed. In other words, standardising a variable does not normalize the distribution of the data.

In a nutshell, standardization:

• centers the mean at 0
• scales the variance at 1
• preserves the shape of the original distribution
• the minimum and maximum values of the different variables may vary
• preserves outliers

Good for algorithms that require features centered at zero.

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