Titlebook: Applied Machine Learning; David Forsyth Textbook 2019 Springer Nature Switzerland AG 2019 machine learning.naive bayes.nearest neighbor.SV

數(shù)量

Ambiguous

High Dimensional Data is hard to plot, though Sect. 4.1 suggests some tricks that are helpful. Most readers will already know the mean as a summary (it’s an easy generalization of the 1D mean). The covariance matrix may be less familiar. This is a collection of all covariances between pairs of components. We use covaria

拍翅

Principal Component Analysistem, we can set some components to zero, and get a representation of the data that is still accurate. The rotation and translation can be undone, yielding a dataset that is in the same coordinates as the original, but lower dimensional. The new dataset is a good approximation to the old dataset. All

爵士樂(lè)

Low Rank Approximationsate points. This data matrix must have low rank (because the model is low dimensional) . it must be close to the original data matrix (because the model is accurate). This suggests modelling data with a low rank matrix.

易于出錯(cuò)

PAEAN