COMPUTER SCIENCE AND ENGINEERING
MACHINE LEARNING
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Question
[CLICK ON ANY CHOICE TO KNOW THE RIGHT ANSWER]
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Choose k to be the smallest value so that at least 99% of the variance is retained.
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Choose k to be 99% of m (k = 0.99*m, rounded to the nearest integer).
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Choose k to be the largest value so that 99% of the variance is retained.
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Use the elbow method.
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Detailed explanation-1: -Correct Answer : Option (D) : Choose k to be the smallest value so that at least 99% of the varinace is retained.
Detailed explanation-2: -Which of the following is a reasonable way to select the number of principal components âk"? Choose k to be 99% of m (k = 0.99*m, rounded to the nearest integer). Choose k to be the largest value so that 99% of the variance is retained.
Detailed explanation-3: -A widely applied approach is to decide on the number of principal components by examining a scree plot. By eyeballing the scree plot, and looking for a point at which the proportion of variance explained by each subsequent principal component drops off. This is often referred to as an elbow in the scree plot.
Detailed explanation-4: -Choosing the Principal Components The common way of selecting the Principal Components to be used is to set a threshold of explained variance, such as 80%, and then select the number of components that generate a cumulative sum of explained variance as close as possible of that threshold.
Detailed explanation-5: -The principal application of PCA is dimension reduction. If you have high dimensional data, PCA allows you to reduce the dimensionality of your data so the bulk of the variation that exists in your data across many high dimensions is captured in fewer dimensions.