Hi I am trying to use randomForest for classification. I am using this code:> set.seed(71) > rf.model <- randomForest(similarity ~ ., data=set1[1:100,],importance=TRUE, proximity=TRUE) Warning message: The response has five or fewer unique values. Are you sure you want to do regression? in: randomForest.default(m, y, ...)> rf.modelCall: randomForest(x = similarity ~ ., data = set1[1:100, ], importance TRUE, proximity = TRUE) Type of random forest: regression Number of trees: 500 No. of variables tried at each split: 10 Mean of squared residuals: 0.1159130 % Var explained: 50.8>As you can see I get a regression model. How can I make sure I get a classification model? Thanks . Stephen -- 2/01/2006 [[alternative HTML version deleted]]
From: Stephen Choularton> > Hi > > I am trying to use randomForest for classification. I am using this > code: > > > set.seed(71) > > rf.model <- randomForest(similarity ~ ., data=set1[1:100,], > importance=TRUE, proximity=TRUE) > Warning message: > The response has five or fewer unique values. Are you sure > you want to > do regression? in: randomForest.default(m, y, ...) > > rf.model > > Call: > randomForest(x = similarity ~ ., data = set1[1:100, ], importance > TRUE, proximity = TRUE) > Type of random forest: regression > Number of trees: 500 > No. of variables tried at each split: 10 > > Mean of squared residuals: 0.1159130 > % Var explained: 50.8 > > > > As you can see I get a regression model. How can I make sure I get a > classification model?By making sure your response variable is a factor, e.g., set1$similarity <- as.factor(set1$similarity) Andy> Thanks . > > Stephen > > -- > > > > 2/01/2006 > > > [[alternative HTML version deleted]] > > ______________________________________________ > R-help at stat.math.ethz.ch mailing list > stat.ethz.ch/mailman/listinfo/r-help > PLEASE do read the posting guide! > R-project.org/posting-guide.html > >