iris.csv : Read data ... Sepal.Length Sepal.Width Petal.Length Petal.Width Species Min. :4.400 Min. :2.20 Min. :1.300 Min. :0.200 setosa :13 1st Qu.:5.325 1st Qu.:2.80 1st Qu.:2.100 1st Qu.:0.625 versicolor:14 Median :6.000 Median :3.00 Median :4.450 Median :1.400 virginica :23 Mean :5.974 Mean :3.05 Mean :4.072 Mean :1.338 3rd Qu.:6.475 3rd Qu.:3.20 3rd Qu.:5.400 3rd Qu.:1.975 Max. :7.900 Max. :4.40 Max. :6.700 Max. :2.500 iris.csv : Select + preproc data ... iris.csv : Importance check ... iris.csv : Train RF (importance) ... Dropped columns: iris.csv : Train RF ... Class weights: 0.9009009 0.925926 1.234568 Call: randomForest(formula = formula, data = to.model, nodesize = 1, classwt = CLASSWT, sampsize = length(to.model[, 1]), proximity = F, na.action = na.roughfix) Type of random forest: classification Number of trees: 500 No. of variables tried at each split: 2 OOB estimate of error rate: 2% Confusion matrix: setosa versicolor virginica class.error setosa 37 0 0 0.00000000 versicolor 0 35 1 0.02777778 virginica 0 1 26 0.03703704 iris.csv : Apply RF ... iris.csv : Calc confusion matrix + gain ... Training cases: --- predicted --- setosa versicolor virginica setosa 37 0 0 versicolor 0 35 1 virginica 0 1 26 setosa versicolor virginica 37 36 27 setosa versicolor virginica Total class errors: 0 0.02777778 0.03703704 0.02 OOB setosa versicolor virginica 0.02000000 0.00000000 0.02777778 0.03703704 setosa versicolor virginica Total gain.vector 37 34 25 96 total gain: 96 (is 96 % of max. gain) Test cases: --- predicted --- setosa versicolor virginica setosa 13 0 0 versicolor 0 14 0 virginica 0 6 17 setosa versicolor virginica 13 20 17 setosa versicolor virginica Total class errors: 0 0 0.2608696 0.12 setosa versicolor virginica Total gain.vector 13 14 11 38 total gain: 38 (is 76 % of max. gain) iris.csv : All done.