APPLICATION OF SUPERVISED LEARNING
CLASSIFICATION IN MACHINE LEARNING
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Question
[CLICK ON ANY CHOICE TO KNOW THE RIGHT ANSWER]
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What is pca components
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Set of all eigen vectors for the projection space
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Matrix of principal components
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Result of the multiplication matrix
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None of the above options
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Explanation:
Detailed explanation-1: -Eigenvalues represent the total amount of variance that can be explained by a given principal component. They can be positive or negative in theory, but in practice they explain variance which is always positive. If eigenvalues are greater than zero, then it’s a good sign.
Detailed explanation-2: -Projection Data The last step of PCA is we need to multiply Q tranpose of Q with the original data matrix in order to get the projection matrix. We go from the (d x k) Q matrix and Q transpose of Q results in d x d dimension. By multiplying the (d x n) X matrix, the projection matrix is d x n.
There is 1 question to complete.