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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PCA will perform outstandingly
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PCA will perform badly
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Can’t Say
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None of above
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Detailed explanation-1: -What will happen when eigenvalues are roughly equal while applying PCA? While applying the PCA algorithm, If we get all eigenvectors the same, then the algorithm won’t be able to select the Principal Components because in such cases, all the Principal Components are equal.
Detailed explanation-2: -Abstract: Principal component analysis (PCA) is widely used as a means of di-mension reduction for high-dimensional data analysis. A main disadvantage of the standard PCA is that the principal components are typically linear combinations of all variables, which makes the results difficult to interpret.
Detailed explanation-3: -Drawbacks of PCA (Principal Component Analysis) PCA is also sensitive to outliers. Such data inputs could produce results that are very much off the correct projection of the data [6]. PCA presents limitations when it comes to interpretability. Since we’re transforming the data, features lose their original meaning.