Call: randomForest(formula = as.factor(response) ~ ., data = trainvals, ntree = 1000, mtry = 6, importance = TRUE, na.action = na.omit) Type of random forest: classification Number of trees: 1000No. of variables tried at each split: 6 OOB estimate of error rate: 9.2%Confusion matrix: 0 1 2 3 4 5 6 7 8 9 class.error0 1764 0 0 0 0 364 0 10 0 0 0.1749298411 0 4081 0 2 1 0 0 10 13 38 0.0154402902 0 1 3307 4 0 0 0 0 0 4 0.0027141133 0 2 0 139 0 1 0 6 0 0 0.0608108114 0 0 0 0 2648 81 0 42 7 0 0.0467962565 461 0 0 1 168 3039 0 199 1 0 0.2145257176 0 0 0 0 0 1 12 115 0 0 0.9062500007 25 9 0 0 74 152 9 5251 20 0 0.0521660658 0 9 0 0 35 12 0 65 569 9 0.1859799719 0 160 1 0 0 0 0 3 33 390 0.335604770