search for: training_set

...nd validation data set tables for future 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 {...

...pass_Selection <- which (runif(num_Passing_Devices) > 0.5) fail_Selection <- which (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_Se...

...se_A48 + Purpose_A49 + P_Status_A91 + P_Status_A92 + P_Status_A93 + P_Status_A94 + Others_A101 + Others_A102 + Others_A103,data=Training_Set, family=binomial(logit)) ########################### Plotting the ROC curve ########...