ガイド連動クイズ · 中レベル

次元削減クイズ

高次元のカバレッジ、PCA 分散と線形投影、近傍マップ、圧縮目標、UMAP グラフ、センタリング、および密度制限を確認します。

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

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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
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
PCA が「線形」手法と呼ばれるのはなぜですか?
- 直線としてプロットされたデータに対してのみ機能します
- データを正確に 1 次元に削減することしかできません
- 最初にデータを並べ替える必要があります
- 加重和 (線形結合) を使用して特徴を結合します。
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
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
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
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
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