Linked 146 Should one remove highly correlated variables before doing PCA? Related 14 What do the arrows in a PCA biplot mean? 25 What are the four axes on PCA biplot? 9 How to interpret this PCA biplot? 5 Interpreting overlapping arrows on a PCA biplot: does it...
Thus I decided to use PCA to decrease the dimensionality. There are two major problem: 1- These variables have different unites some are time values and some are number of repetitions. can I use the row data for PCA, or do I need to normalize them beforehand? In the latter case, what...
Learn about factor analysis - a simple way to condense the data in many variables into a just a few variables.
but I'm clueless how to calculate the rest. My only idea is to convert the 3d array to a 2d matrix of co-ordinates, multiply by a rotation matrix, and convert back. But I'd rather work directly with the 3d array.
Knowing how to interpret data and derive insights is crucial in this field. Concepts like statistical significance, distribution, regression, and likelihood play a significant role in different AI applications. A willingness to learn: AI is a rapidly evolving field with new advancements, techniques, ...
1, as it is difficult to interpret the results at all without making several of these assumptions. Here we use simulated and real data to illustrate how following this protocol can lead to inference of false histories, and how badMIXTURE can be used to examine model fit and avoid common ...
PlottingPCAwith several components; Conclusion We know that massive datasets are increasingly widespread in all sorts of disciplines. Therefore, to interpret such datasets, thedimensionality is decreasedso that the highly related data can be preserved. ...
Then I do PCA to get the projection of this data matrix into the space spanned by two first components. Then I plot them and have a graph To me, the graph only shows the coordinate of our original data in the principal plane. Could you please elaborate on how to interpret (or get ...
How do you decide on the number of PCs to use? If I go by the usual methods of choosing PCs I would do an elbow plot and take a point where the variance does not increase much more. Would that be a reasonable way to choose the number of PCs?
Learn how to detect multicollinearity in regression models using the variance inflation factor (VIF), a key diagnostic tool. This tutorial explains how VIF is calculated, how to interpret its values, and techniques for addressing high VIF to improve the reliability of your regression modeling. ...