Training the random Forrest with these settings took 744 seconds on a modern server with 32 GB RAM and can be summarized like this:
Call:
randomForest(formula = my.form, data = trainframe, ntree = my.ntree, mtry = my.mtry, get.importance = get.importance.Q)
Type of random forest: classification
Number of trees: 200
No. of variables tried at each split: 10
OOB estimate of error rate: 9.16%
Confusion matrix:
FALSE-FALSE-FALSE-FALSE FALSE-FALSE-FALSE-TRUE FALSE-FALSE-TRUE-FALSE FALSE-FALSE-TRUE-TRUE
FALSE-FALSE-FALSE-FALSE 180960 1707 1943 363
FALSE-FALSE-FALSE-TRUE 5353 35693 535 474
FALSE-FALSE-TRUE-FALSE 3244 426 43996 86
FALSE-FALSE-TRUE-TRUE 726 221 185 23159
FALSE-TRUE-FALSE-FALSE 6139 185 188 23
FALSE-TRUE-FALSE-TRUE 280 10 94 35
TRUE-FALSE-FALSE-FALSE 689 72 67 661
TRUE-TRUE-FALSE-FALSE 5318 546 114 23
FALSE-TRUE-FALSE-FALSE FALSE-TRUE-FALSE-TRUE TRUE-FALSE-FALSE-FALSE TRUE-TRUE-FALSE-FALSE class.error
FALSE-FALSE-FALSE-FALSE 4217 89 468 3416 0.06317462
FALSE-FALSE-FALSE-TRUE 191 11 65 365 0.16384379
FALSE-FALSE-TRUE-FALSE 126 29 114 36 0.08450382
FALSE-FALSE-TRUE-TRUE 18 62 703 45 0.07802858
FALSE-TRUE-FALSE-FALSE 64374 79 0 1811 0.11572961
FALSE-TRUE-FALSE-TRUE 74 5825 0 103 0.09282043
TRUE-FALSE-FALSE-FALSE 6 0 21464 21 0.06597041
TRUE-TRUE-FALSE-FALSE 2149 142 4 61475 0.11890327
The accuracy, assessed with the 10% validation set, is similar.
Confusion Matrix and Statistics
Reference
Prediction FALSE-FALSE-FALSE-FALSE FALSE-FALSE-FALSE-TRUE FALSE-FALSE-TRUE-FALSE FALSE-FALSE-TRUE-TRUE
FALSE-FALSE-FALSE-FALSE 20130 556 361 66
FALSE-FALSE-FALSE-TRUE 225 4034 48 21
FALSE-FALSE-TRUE-FALSE 200 54 4916 25
FALSE-FALSE-TRUE-TRUE 31 53 10 2437
FALSE-TRUE-FALSE-FALSE 419 16 10 2
FALSE-TRUE-FALSE-TRUE 10 1 3 3
TRUE-FALSE-FALSE-FALSE 57 6 16 78
TRUE-TRUE-FALSE-FALSE 402 48 1 4
Reference
Prediction FALSE-TRUE-FALSE-FALSE FALSE-TRUE-FALSE-TRUE TRUE-FALSE-FALSE-FALSE TRUE-TRUE-FALSE-FALSE
FALSE-FALSE-FALSE-FALSE 643 35 85 587
FALSE-FALSE-FALSE-TRUE 18 1 7 55
FALSE-FALSE-TRUE-FALSE 19 11 8 8
FALSE-FALSE-TRUE-TRUE 1 2 66 1
FALSE-TRUE-FALSE-FALSE 7267 10 3 239
FALSE-TRUE-FALSE-TRUE 10 656 1 15
TRUE-FALSE-FALSE-FALSE 0 0 2417 1
TRUE-TRUE-FALSE-FALSE 203 7 4 6822
Overall Statistics
Accuracy : 0.9108
95% CI : (0.9084, 0.9132)
No Information Rate : 0.4018
P-Value [Acc > NIR] : < 2.2e-16
Kappa : 0.8836
Mcnemar's Test P-Value : < 2.2e-16
Statistics by Class:
Class: FALSE-FALSE-FALSE-FALSE Class: FALSE-FALSE-FALSE-TRUE Class: FALSE-FALSE-TRUE-FALSE
Sensitivity 0.9374 0.84606 0.91631
Specificity 0.9270 0.99230 0.99324
Pos Pred Value 0.8961 0.91495 0.93799
Neg Pred Value 0.9566 0.98503 0.99069
Prevalence 0.4018 0.08921 0.10038
Detection Rate 0.3766 0.07548 0.09198
Detection Prevalence 0.4203 0.08250 0.09806
Balanced Accuracy 0.9322 0.91918 0.95477
Class: FALSE-FALSE-TRUE-TRUE Class: FALSE-TRUE-FALSE-FALSE Class: FALSE-TRUE-FALSE-TRUE
Sensitivity 0.92451 0.8905 0.90859
Specificity 0.99677 0.9846 0.99918
Pos Pred Value 0.93695 0.9123 0.93848
Neg Pred Value 0.99609 0.9803 0.99875
Prevalence 0.04932 0.1527 0.01351
Detection Rate 0.04560 0.1360 0.01227
Detection Prevalence 0.04867 0.1491 0.01308
Balanced Accuracy 0.96064 0.9375 0.95389
Class: TRUE-FALSE-FALSE-FALSE Class: TRUE-TRUE-FALSE-FALSE
Sensitivity 0.93284 0.8828
Specificity 0.99689 0.9854
Pos Pred Value 0.93864 0.9107
Neg Pred Value 0.99658 0.9803
Prevalence 0.04848 0.1446
Detection Rate 0.04522 0.1276
Detection Prevalence 0.04818 0.1402
Balanced Accuracy 0.96487 0.9341