search for: validation_set

...uture use in a machine learning algorithm using the code: #generate probabilities to divide up training / validation data sets randomly device_Prob_Vector <- runif(num_Devices) #NULL-initialize training and validation sets. This seems like a bit of a hack... training_Set <- measurements[0] validation_Set <- measurements[0] #divide up the training and validation data sets from measurements. for ( i in 1:num_Devices) { if ( device_Prob_Vector[i] > 0.5 ) { training_Set <- rbind(training_Set, measurements[i,]) } else { validation_Set <- rbind(val...

...(runif(num_Failing_Devices) > 0.5) # ... which are then used to establish training and validation data sets with each set containing # ~50% of "good" and ~50% of "bad" data points training_Set <- rbind(passing_Devices[pass_Selection,],failing_Devices[fail_Selection,]) validation_Set <- rbind(passing_Devices[-pass_Selection,], failing_Devices[-fail_Selection,]) # columns 2 to 66 are independent variables x <- training_Set[,2:66] # and 67 to 137 are dependent y <- training_Set[,67:137] model <- mars(x,y) x_v <- validation_Set[,2:66] y_v <- validation_Se...