Feature scaling

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Feature scaling is a technique, performed in Pre-Processing stage, that is done on the independent variables making the associated values of the particular variable  in a fixed range.


Why we need 'Feature Scaling' ?


Some machine learning models give more emphasis on the data of the independent variables over variables itself. That's why Scaling plays a very crucial role in such cases where we have to scale our data according some techniques(discussed later). By applying Feature Scaling we are actually giving the same priority to all the independent variables present in the dataset.


Machine learning models that are affected by Feature Scaling -


Machine learning models or algorithms, involved in distance measurement, are affected by the feature scaling. some of them are 
  • KNN ( K-Nearest Neighbors ) [Used for Regression/Classification]
  • PCA ( Principal component Analysis ) [used for Dimensionality reduction  ]
  • K-means algorithm [used for Clustering(Hierarchical clustering) ]
Let's have a look to the full list

Machine learning models that are affected by Feature Scaling










Different Techniques used for Feature Scaling -


There are many techniques involved for feature scaling. But two of them are mostly used. They are 
  • Min-Max Scalar
  • Standard Scalar

Min-Max Scalar

  • It is a scaling technique used for Normalizing the data.
  • This Min-Max Scalar are greatly affected by the outliers present in the data.
  • it assumes that data is not distributed as  Gaussian nature.
Min-Max Scalar

Standard Scalar

  • It is a scaling technique used for standardize the data.
  • Standard Scalar are not affected by the outliers present in the data 
  • It assumes that data is normally distributed.
Standard Scalar

Difference between Standardization and Normalization -


Standardization means making the mean of the values 0 and variance of them 1. On the other hand,
Normalization means making the values lie in between 0 and 1.

Difference between Standardization and Normalization -



There are Some other scaling techniques such as

  •  Max Abs Scaler
  • Robust Scaler
  • Quantile Transformer Scaler
  • Power Transformer Scaler
  • Unit Vector Scaler
For more info please go through this


Python practice

  • First importing required library and checking the correlation matrix of the data-frame.
required library importing and data-frame creation and checking correlation matrix
  • Then applying Standard Scalar() function and checking the correlation matrix
correlation coefficient matrix
  • Then applying Min-Max Scalar() function and checking the correlation matrix
correlation coefficient matrix


Note: The scaling technique does not affect the covariance matrix, correlation matrix etc.

Conclusion

Form this post we have learnt about the concepts related to Feature Scaling which is very much a necessary steps while preprocessing the data in  some machine learning model and algorithms.

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