题目内容
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A、New feature space by PCA is constructed by choosing the most important features you think.
B、New feature space by PCA is constructed by normalizing input data.
C、New feature space by PCA is constructed by selecting features randomly.
D、New feature space by PCA is constructed by eliminating the weak components to reduce the size of the data.
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更多“How to construct new feature s…”相关的问题
A、contributed
B、dedicated
C、distributed
D、attributed
A、Histogram analysis and Binging are both unsupervised methods.
B、Clustering analysis only belongs to top-down split.
C、Interval merging by c2 Analysis can be applied recursively.
D、Decision-tree analysis is Entropy-based discretization.
A、Min-max normalization
B、Simple scaling
C、Z-score normalization
D、Normalization by decimal scaling
A、Simple random sample without replacement
B、Simple random sample with replacement
C、Stratified sample
D、All above
A、Equal-width (distance) partitioning is the most straightforward, but outliers may dominate presentation.
B、Equal-depth (frequency) partitioning Divides the range into N intervals, each containing approximately same number of samples.
C、The interval of the former one is not equal.
D、The number of tuples is the same when using the latter one.
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