COMPUTER SCIENCE AND ENGINEERING
MACHINE LEARNING
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Question
[CLICK ON ANY CHOICE TO KNOW THE RIGHT ANSWER]
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When data comes from sensor
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When you are using a Linux machine
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When your data set is larger than 500GB
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When you have larger set of features with similar characteristics
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Detailed explanation-1: -When dealing with high dimensional data, it is often useful to reduce the dimensionality by projecting the data to a lower dimensional subspace which captures the “essence” of the data. This is called dimensionality reduction.
Detailed explanation-2: -Dimensionality reduction brings many advantages to your machine learning data, including: Fewer features mean less complexity. You will need less storage space because you have fewer data. Fewer features require less computation time.
Detailed explanation-3: -you can reduce dimensionality by limiting the number of principal components to keep based on cumulative explained variance. The PCA transformation is also dependent on scale, so you should normalize your dataset first. PCA is a find linear correlations between the features given.