Perhaps you are disappointed with the performance of the random forest regression model, but the true power of the technique is in the classification problems. Let's get started with the breast cancer diagnosis data. The procedure is nearly the same as we did with the regression problem:
> set.seed(123) > rf.biop <- randomForest(class ~. , data = biop.train) > rf.biop Call: randomForest(formula = class ~ ., data = biop.train) Type of random forest: classification Number of trees: 500 No. of variables tried at each split: 3 OOB estimate of error rate: 3.16% Confusion matrix: benign malignant class.error benign 294 8 0.02649007 malignant 7 165 0.04069767
The OOB error rate is 3.16%. Again, this is with all ...
