PCA is a method of dimensional reduction. When a machine learning method has a large number of features, we must choose the features that contribute the most and ignore the datasets that are less significant. This is where PCA comes into play. Consequently, PCA is an unsupervised method for choosing useful datasets from a huge […]
pca
R Exercise: Working with PCA and Dimensionality Reduction
Check the data mtcars with head and save a new data as mtcars.subset after dropping two non-numeric (binary) variables for PCA analysis data <- mtcars head(data) ## mpg cyl disp hp drat wt qsec vs am gear carb ## Mazda RX4 21.0 6 160 110 3.90 2.620 16.46 0 1 4 4 ## Mazda RX4 […]