how to do
I notice that AUTO CLEANSING has 2 options: PCA and norm
Find more posts tagged with
Sort by:
1 - 1 of
11
Sort by:
1 - 1 of
11
Hello @JoeJoe
Yes, you will have two options, it is your choice to perform none of these or any one of these or both. PCA is used for dimensionality reduction, in case you have many attributes (features/columns) and you want to reduce the total features for low complexity or better run times of algorithm then PCA is a useful method. PCA will create new features (Principal components) from existing features, one issue with using PCA is to understand what these features (Principal components) represent in the real world.
The second option normalization is useful in case of features (attributes/columns) with highly varying ranges. For example, you have an age column with 1 to 100 years and income column in 10000 to 100000 then normalization is suggested as the numeric ranges in columns are highly varying and these variations will have an impact on the algorithm.
Hope this helps
Yes, you will have two options, it is your choice to perform none of these or any one of these or both. PCA is used for dimensionality reduction, in case you have many attributes (features/columns) and you want to reduce the total features for low complexity or better run times of algorithm then PCA is a useful method. PCA will create new features (Principal components) from existing features, one issue with using PCA is to understand what these features (Principal components) represent in the real world.
The second option normalization is useful in case of features (attributes/columns) with highly varying ranges. For example, you have an age column with 1 to 100 years and income column in 10000 to 100000 then normalization is suggested as the numeric ranges in columns are highly varying and these variations will have an impact on the algorithm.
Hope this helps
Yes, you will have two options, it is your choice to perform none of these or any one of these or both. PCA is used for dimensionality reduction, in case you have many attributes (features/columns) and you want to reduce the total features for low complexity or better run times of algorithm then PCA is a useful method. PCA will create new features (Principal components) from existing features, one issue with using PCA is to understand what these features (Principal components) represent in the real world.
The second option normalization is useful in case of features (attributes/columns) with highly varying ranges. For example, you have an age column with 1 to 100 years and income column in 10000 to 100000 then normalization is suggested as the numeric ranges in columns are highly varying and these variations will have an impact on the algorithm.
Hope this helps