a) In each of the following plots, a training dataset of data points X in R^2 labeled
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a) In each of the following plots, a training dataset of data points X in R^2 labeled either + or - is given, where the original features are the coordinates (x,y). You can assume that the data is origin-centered. For each of the two training sets below, answer the following questions:
- Draw a simple simple recreation of each of the two datasets below (no need for exact precision) and draw all the principal components (eyeball it).
- For each dataset, can we correctly classify the labels by using a halfspace after projecting onto one of the principle components? If so, which principal component should we project onto? If not, explain in 1-2 sentences why it is not possible.
b). Is it possible to have a data set in R^2 that is linearly separable by a halfspace in R^2 but is not linearly separate after projecting onto either of the two principal components?
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Introduction To Management Science and Business Analytics A Modeling And Case Studies Approach With
ISBN: 9781260716290
7th Edition
Authors: Frederick S. Hillier, Mark S. Hillier
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