PCA is a statistical technique that transforms a dataset defined by possibly correlated variables (whose noise negatively affects the performance of your model) into a set of uncorrelated variables, called principal components. Read more below.
The new BigML release is here! Join us on Thursday, December 20, 2018, at 10:00 AM PST (Portland, Oregon. GMT -08:00) / 07:00 PM CET (Valencia, Spain. GMT +01:00) for a FREE live webinar to discover the latest addition to the BigML platform. We will be showcasing Principal Component Analysis (PCA), a key unsupervised Machine Learning technique used to transform a given dataset in order to yield uncorrelated features and reduce dimensionality. PCA is most commonly applied in fields with high dimensional data including bioinformatics, quantitative finance, and signal processing, among others.
Principal Component Analysis (PCA), available on the BigML Dashboard, API and WhizzML for automation as of December 20, 2018, is a statistical technique that transforms a dataset defined by possibly correlated variables (whose noise negatively affects the performance of your model) into a set of uncorrelated variables, called principal components. This technique is used as…
View original post 355 more words
BLACK BOX PARADOX 