CODEWITHSUNDEEP
pythonmachine-learningscikit-learnregression
Gissipi_453
Gissipi_453

Reputation: 1340

The use of feature scaling in scikit learn

I'm studing machine learning from here and the course uses 'scikit learn' from regression - https://www.udemy.com/machinelearning/

I can see that for some training regression algorithms, the author uses feature scaling and for some he doesn't because some 'scikit learn' regression algorithms take care of feature scaling by themselves.

How to know in which training algorithm we need to do feature scaling and where we don't need to ?

Upvotes: 0

Views: 1231

Answers (3)

Raj Mehta
Raj Mehta

Reputation: 324

A very simple answer. Some algorithm does the feature scaling even if you don't and some do not. So, if the algorithm does not, you need to manually scale the features.

You can google which algorithm does the feature scaling, but its good to be safe by manually scaling the feature. Always make sure, the features are scaled, otherwise, the algorithm would give output offset to ideal.

Upvotes: 1

Syed Rafay
Syed Rafay

Reputation: 1500

It depends on the algorithm you are using and your dataset.

  • Support Vector Machines (SVM), these models converge faster if you scale your features . The main advantage of scaling is to avoid attributes in greater numeric ranges dominating those in smaller numeric ranges

  • In K-means clustering, you find out the Euclidean distance for clustering different data points together. Thus it seems to be a good reason to scale your features so that the centroid doesn't get much affected by the large or abnormal values.

  • In case of regression, scaling your features will not be of much help since the relation of coefficients between original dataset and the relation of coefficients between scaled dataset will be the same.

  • In case of Decision Trees, they don't usually require feature scaling.

  • In case of models which have learning rates involved and are using gradient descent, the input scale does effect the gradients. So feature scaling would be considered in this case.

Upvotes: 3

Zachary Drescher
Zachary Drescher

Reputation: 56

No machine learning technique needs feature scaling, for some algoirthms scaled inputs make the optimizing easier on the computer which results in faster training time.

Typically, algorithms that leverage distance or assume normality benefit from feature scaling. https://medium.com/greyatom/why-how-and-when-to-scale-your-features-4b30ab09db5e

Upvotes: 3

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