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1 . Consider the following supervised dataset consisting of 1 0 observations, each with two features ( observable data is in X ) and an
Consider the following supervised dataset consisting of observations, each with two features observable data is in X and an associated label in Y :
X; ; ; ; ; ; ; ; ; Y; ; ; ; ; ; ; ; ;
a Compute the average weighted entropy of the class label in the subsets created by splitting the dataset based on the value of the first feature. You may assume that the features are categoricial ptsb Now make the same computation, but as if we created subsets using the second feature! ptsc Which feature is more discriminating based on results in Part a That is which feature provides better class separation ptsd What are the principle components of the observed data X For this and the next part you may assume that the features are continuous and therefore should zscore them. Make sure your final principle components are all unit length. You MAY use a utility function like e i g or s v d to determine these ptse In your own words, describe these axis in terms of a conventional D Cartesian Coordinate system ptsf If we were to project our data down to D using the principle component, what would the new data matrix X be ptsTheory Questions
Consider the following supervised dataset consisting of observations, each with two features
observable data is in and an associated label in :
a Compute the average weighted entropy of the class label in the subsets created by splitting
the dataset based on the value of the first feature. You may assume that the features are
categoricial
b Now make the same computation, but as if we created subsets using the second feature!
c Which feature is more discriminating based on results in Part a That is which feature
provides better class separation
d What are the principle components of the observed data For this and the next
part you may assume that the features are continuous. Make sure your final principle
components are all unit length. You MAY use a utility function like eig or svd to
determine these
e In your own words, describe these axis in terms of a conventional D Cartesian Coordinate
system pts
f If we were to project our data down to D using the principle component, what would
the new data matrix be
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