DATABRICKS-CERTIFIED-PROFESSIONAL-DATA-SCIENTIST · Question #102
You have collected the 100's of parameters about the 1000's of websites e.g. daily hits, average time on the websites, number of unique visitors, number of returning visitors etc. Now you have find…
The correct answer is A. PCA (Principal component analysis). Principal component analysis . or PCA, is a technique for taking a dataset that is in the form of a set of tuples representing points in a high-dimensional space and finding the dimensions along which the tuples line up best. The idea is to treat the set of tuples as a matrix M…
Question
You have collected the 100's of parameters about the 1000's of websites e.g. daily hits, average time on the websites, number of unique visitors, number of returning visitors etc. Now you have find the most important parameters which can best describe a website, so which of the following technique you will use
Options
- APCA (Principal component analysis)
- BLinear Regression
- CLogistic Regression
- DClustering
How the community answered
(22 responses)- A77% (17)
- B14% (3)
- C5% (1)
- D5% (1)
Explanation
Principal component analysis . or PCA, is a technique for taking a dataset that is in the form of a set of tuples representing points in a high-dimensional space and finding the dimensions along which the tuples line up best. The idea is to treat the set of tuples as a matrix M and find the eigenvectors for MMT or M T M . The matrix of these eigenvectors can be thought of as a rigid rotation in a high- dimensional space. When you apply this transformation to the original data, the axis corresponding to the principal eigenvector is the one along which the points are most "spread out,11 More precisely this axis is the one along which the variance of the data is maximized. Put another way, the points can best be viewed as lying along this axis, with small deviations from this axis.
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