Jacob Mick

136 reputation

	136 reputation	bio	website		visits	member for	1 year, 2 months
4 badges		location			seen	yesterday

15 Actions

Mar 18	comment	The multiple regression model - The minimum sum of squares The `rank' of M is a scalar. Intuitively it is the size of the basis of independent vectors used to describe M. You come to this conclusion by realizing that M = M dot M.
Mar 18	comment	The multiple regression model - The minimum sum of squares M = (I − X(X′X)^{−1}X′)
Mar 17	answered	The multiple regression model - The minimum sum of squares
Jan 26	answered	classifiers providing probability of being correct
Jun 8	revised	Neural networks vs support vector machines: are the second definitely superior? added 21 characters in body
Jun 8	answered	Neural networks vs support vector machines: are the second definitely superior?
Apr 27	awarded	Supporter
Mar 4	comment	How to know when to stop reducing dimensions with PCA? Right you're very correct. It is highly dependent on that assumption. Discarding any of the nonsingular eigenvectors requires making assumptions about the manifold that the data lies in. If there isn't a extreme class imbalance then it is reasonable to assume that a low-dimensional manifold will describe the relevant signal.
Mar 3	awarded	Teacher
Mar 3	revised	How to know when to stop reducing dimensions with PCA? edited body
Mar 3	awarded	Editor
Mar 3	revised	How to know when to stop reducing dimensions with PCA? deleted 6 characters in body
Mar 3	answered	How to know when to stop reducing dimensions with PCA?
Mar 2	awarded	Student
Mar 2	asked	Graphical nominal model