1. A method for determining one or more predictions for a big data set, the method being implemented by one or more processors and comprising: receiving at least a portion of the big data set; implementing a model that includes one or more classes, the model associating a probability with each class of the one or more classes; representing, for each of the one or more classes of the model, a set of input variables of the big data set as a matrix with non-zero values only provided as diagonal entries; determining a most likely class for each input variable based at least in part on inverting the matrix; and determining one or more predictions for one or more output variables based at least in part on the most likely class of one or more input variables from the set of input variables.