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问题 1 的 8

Which data-coverage challenge is associated with increasing dimension in the curse of dimensionality?

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  1. Which data-coverage challenge is associated with increasing dimension in the curse of dimensionality?
    • Every added coordinate necessarily improves every model
    • All high-dimensional distances are identical by definition
    • A much larger space can require many more observations to cover densely
    • Every model becomes faster without changing its computation
  2. Which directions does PCA prioritize when selecting principal components of centered input data?
    • Directions guaranteed to have the greatest target-label accuracy
    • Directions of high input variance
    • Directions chosen only from column names
    • Directions of minimum variance in every setup
  3. 为什么 PCA 被称为“线性”方法？
    • 它仅适用于绘制为直线的数据
    • 它只能将数据减少到恰好一维
    • 需要先对数据进行排序
    • 它使用加权和（线性组合）来组合特征
  4. Which aspect of data do t-SNE and UMAP commonly emphasize in exploratory low-dimensional embeddings?
    • Neighborhood relationships
    • A guarantee of all original pairwise distances
    • An exact encryption of every input file
    • A proof that every visible group is a true category
  5. Why might a team evaluate a smaller representation before fitting a downstream model?
    • To guarantee that no useful information is ever lost
    • To compare computational cost and predictive behavior with fewer coordinates
    • To eliminate the need for held-out evaluation
    • To prove that every discarded component is noise
  6. Which structure does UMAP use when constructing its representation of nearby input observations?
    • A required list of supervised document labels
    • A file-format encryption key
    • A neighborhood graph
    • A guarantee that all cluster densities are preserved
  7. By default, how does the stated scikit-learn PCA transformation preprocess feature means and scales?
    • It centers features but does not scale each to unit variance
    • It both centers and scales every feature to unit variance automatically
    • It scales each feature to unit variance but leaves its mean unchanged
    • It divides every feature by its range without centering
  8. Why should visual compactness in an ordinary UMAP plot not be treated as proof of original-space cluster density?
    • UMAP guarantees exact preservation of every original density ratio
    • Plot compactness is automatically calibrated to population frequency
    • The projection preserves every original pairwise distance exactly
    • UMAP does not reliably preserve density in its ordinary embedding