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

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

  • 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!

  • 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!

     

  • 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

  • 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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