library(CORElearn) ## Input data data(frbsData) data.train <- data.frame(frbsData$GasFurnance.dt[1 : 204, ]) data.fit <- data.frame(data.train[, 1 : 2]) data.tst <- data.frame(frbsData$GasFurnance.dt[205 : 292, 1 : 2]) real.val <- matrix(frbsData$GasFurnance.dt[205 : 292, 3], ncol = 1) range.data<-matrix(c(-2.716, 2.834, 45.6, 60.5, 45.6, 60.5), nrow=2) colnames(data.train) <- c("d1", "d2", "d3") colnames(data.tst) <- c("d1", "d2") data.test <- data.frame(frbsData$GasFurnance.dt[205 : 292, ]) colnames(data.test) <- c("d1", "d2", "d3") ## generate model using classification tree with default parameters (for simplicity). object.LWR <- CoreModel(d3 ~., data.train, model = "regTree", modelTypeReg = 1) pred <- predict(object.LWR, data.test, type="probability") benchmark <- cbind(real.val, pred) colnames(benchmark) <- c("real", "tree") print(benchmark) residuals <- (real.val - pred) MSE <- mean(residuals^2) RMSE <- sqrt(mean(residuals^2)) SMAPE <- mean(abs(residuals)/(abs(real.val) + abs(pred))/2)*100 err <- c(MSE, RMSE, SMAPE) names(err) <- c("MSE", "RMSE", "SMAPE") print("Error Measurement: ") print(err)