Dimensionality Reduction Techniques - PCA, Kernel-PCA and LDA Using Python

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Dimensionality Reduction Techniques - PCA, Kernel-PCA and LDA Using Python

Prashant Tyagi

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May 24, 2021 at 12:00 am

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Comments posted to this topic are about the item Dimensionality Reduction Techniques - PCA, Kernel-PCA and LDA Using Python

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matthew.farrugia Grasshopper Points: 22 More actions · Answer 1

Hi, great article! Am I missing something, as at one point you say:

"Generally we can choose to normalize (normalization) when the data is normally distributed, and scale (standardization) when the data is not normally distributed"

and then you follow this by stating:

"Further, we use fit_transform() along with the assigned object 'sc' to transform the data and standardize it. Standardization is only applicable on the data values that follow a Normal Distribution."

Appreciate some clarity on this. Thanks!

Prashant Tyagi SSC Veteran Points: 246 More actions · Answer 2

Thank you Mathew for pointing that out. It should be "do not" follow a normal distribution instead of "follow" a normal distribution.

When should we use Standardization and Normalization?

Generally you should normalize (normalization) when the data is normally distributed, and scale (standardization)

when the data is not normally distributed. In doubt, you should go for standardization. However

what is commonly done is that the two scaling methods are tested.

Hope this helps!

Thank you very much and appreciate the question!

Riskworks Mr or Mrs. 500 Points: 592 More actions · Answer 3

Thanks, great article.

Is the dataset available for this? I don't see it in this article of in any prequel.

Thanks. Again.

John

John A. Byrnes

Prashant Tyagi SSC Veteran Points: 246 More actions · Answer 4

Hi Riskworks,

Thank you very much for your comments.

Please find the dataset attached herewith for your ready reference!

Have a great day ahead!

Warm Regards

Prashant Tyagi

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