Maswali yanayounganishwa na mwongozo · Kati Kiwango

Maswali ya Kupunguza Dimensionality

Kagua ufikiaji wa hali ya juu, tofauti za PCA na makadirio ya mstari, ramani za ujirani, malengo ya mgandamizo, grafu za UMAP, kuweka katikati, na mipaka ya msongamano.

Njia za mwongozo zinazohusianaKupunguza Dimensionality
Swali 1 ya 8

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

Hakiki kila swali8 maswali

Majibu na maelezo huonekana unapojibu maswali.

  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. Kwa nini PCA inaitwa njia ya 'linear'?
    • Inafanya kazi tu kwenye data iliyopangwa kama mistari iliyonyooka
    • Inaweza tu kupunguza data kwa kipimo kimoja haswa
    • Inahitaji data kupangwa kwanza
    • Inachanganya vipengele kwa kutumia hesabu zilizo na uzani (mchanganyiko wa mstari)
  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