Question

When we run the PCA dimensionality reduction algorithm, its effect is similar to what we will get if we were to load the dataset in Excel and delete some columns (dimensions). For instance, if we load a 10 column/dimensional dataset and use PCA to reduce it to 2 dimensions, the result is similar to loading it in Excel and deleting 8 columns. a. True b. False

Answer 1

b. False

The statement is not entirely accurate. PCA (Principal Component Analysis) is a dimensionality reduction technique, but its effect is not the same as simply deleting columns in Excel. When you apply PCA to a dataset, it doesn't just remove columns; instead, it transforms the data into a new set of orthogonal (uncorrelated) variables called principal components. These components capture the most important patterns and variations in the data while retaining as much information as possible.

So, when you reduce a 10-dimensional dataset to 2 dimensions using PCA, you're not merely deleting 8 columns; you're creating two new composite dimensions (the first two principal components) that summarize the data in a more meaningful way. These components are linear combinations of the original variables and may capture complex relationships that wouldn't be apparent by simply removing columns in Excel.