I have applied PCA attribute evaluator (with maximum attribute names as 5, and variance covered as 0.95) in WEKA for the heart disease dataset which consists of 13 predictors and one target class variable. Then I got 18 principal components. Is this considered as dimensionality reduction? if not what exactly the PCA does on this dataset?
Dear Karna Vishnu Vardhana Reddy ,
Dimensionality reduction involves reducing the number of input variables or columns in modeling data. PCA is a technique from linear algebra that can be used to automatically perform dimensionality reduction.
https://machinelearningmastery.com/principal-components-analysis-for-dimensionality-reduction-in-python/#:~:text=Dimensionality%20reduction%20involves%20reducing%20the,to%20automatically%20perform%20dimensionality%20reduction.
Regards,
Shafagat
Madam @Shafagat Mahmudova, but why in my case the 13 columns become 18?
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