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 happens when you get features in lower dimensions using PCA?1)The features will still have interpretability 2)The features will lose interpretability 3)The features must carry all information present in data 4)The features may not carry all information present in data
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1 and 3
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1 and 4
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2 and 3
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2 and 4
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Explanation:
Detailed explanation-1: -PCA generally tries to find the lower-dimensional surface to project the high-dimensional data. PCA works by considering the variance of each attribute because the high attribute shows the good split between the classes, and hence it reduces the dimensionality.
Detailed explanation-2: -Disadvantages: Loss of information: PCA may lead to loss of some information from the original data, as it reduces the dimensionality of the data.
Detailed explanation-3: -PCA can be used for projecting and visualizing data in lower dimensions. Explanation: Sometimes it is very useful to plot the data in lower dimensions. We can take the first 2 principal components and then use visualization of the data using a scatter plot.
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