u201cGroup Labelsu201d is a handy tool if you are collecting data for multiple classes and then want to group a few classes into one. For example, if you are classifying human activities as WALKING, RUNNING, BIKING, and IDLE, you may have collected data of human subjects sitting, standing, and working on the computer. You can group sitting, standing, and working on the computer classes as u2018IDLEu2019. During live classification, if human subject is sitting, standing or working on the computer, they will be classified as u2018IDLEu2019.   Before grouping labels, we recommend building a model with all labels and then checking the Matthews Correlation Coefficient (MCC) table in the Results section. The pairs which have low MCC valuesu00a0are generally not separable. We recommend grouping these labels for best performance.