FALSE<\/strong>.<\/p>\nPrincipal component analysis (PCA) is a statistical procedure that uses an orthogonal transformation to convert a set of observations of possibly correlated variables into a set of values of linearly uncorrelated variables called principal components. The number of principal components is less than or equal to the number of original variables.<\/p>\n
In other words, PCA can be used to reduce the dimensionality of a dataset by finding a set of new variables that are uncorrelated with each other and that capture as much of the variation in the original data as possible. The new variables are called principal components, and they are ordered in such a way that the first principal component captures the most variation in the data, the second principal component captures the second most variation, and so on.<\/p>\n
The number of principal components is always less than or equal to the number of original variables. This is because the principal components are orthogonal to each other, which means that they are uncorrelated. If there are more principal components than original variables, then some of the principal components will be correlated with each other, which defeats the purpose of PCA.<\/p>\n
Therefore, the answer to the question “In PCA the number of input dimensions are equal to principal components” is FALSE<\/strong>.<\/p>\n","protected":false},"excerpt":{"rendered":"[amp_mcq option1=”TRUE” option2=”nan” option3=”nan” option4=”nan” correct=”option1″]<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[729],"tags":[],"class_list":["post-57617","post","type-post","status-publish","format-standard","hentry","category-machine-learning","no-featured-image-padding"],"yoast_head":"\n
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