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 is an unsupervised method
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It searches for the directions that data have the largest variance
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Maximum number of principal components <= number of features
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All principal components are orthogonal to each other
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Detailed explanation-1: -The most popularly used dimensionality reduction algorithm is Principal Component Analysis (PCA). Which of the following is/are true about PCA? Explanation: All options are self-explanatory.
Detailed explanation-2: -Principal Component Analysis(PCA) is one of the most popular linear dimension reduction algorithms. It is a projection based method that transforms the data by projecting it onto a set of orthogonal(perpendicular) axes.
Detailed explanation-3: -PCA helps us to identify patterns in data based on the correlation between features. In a nutshell, PCA aims to find the directions of maximum variance in high-dimensional data and projects it onto a new subspace with equal or fewer dimensions than the original one.
Detailed explanation-4: -The principal components in PCA are linear combinations of the initial variables that maximize the variance explained by the data. Principal components are calculated using the correlation matrix.