Create 3.r

3.r

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+
+
+
+library(caret)
+library(mlbench)
+library(randomForest)
+
+set.seed(200)
+simulated <- mlbench.friedman1(200,sd=1)
+simulated <- cbind(simulated$x,simulated$y)
+simulated <- as.data.frame(simulated)
+colnames(simulated)[ncol(simulated)] <- "y"
+
+model1 = randomForest(y ~ .,data=simulated, importance=TRUE,ntree = 1000)
+rfImp1 = varImp(model1,scale = FALSE)
+rfImp1 = rfImp1[ order(-rfImp1), ,drop = FALSE]
+print("random forest ( no correlatedpredictors")
+print(rfImp1)
+
+## part b now add an additional predictor that is highly correlated with one of the informative predctors
+
+simulated$duplicate1 = simulated$V1 + rnorm(200) * 0.1
+cor(simulated$duplicate1, simulated$V1)
+
+model2 = randomForest( y ~ ., data = simulated, importance = TRUE, ntree = 1000)
+rfImp2 = varImp(model2, scale = FALSE)
+rfImp2 = rfImp2[order(-rfImp2), , drop = FALSE]
+print("random forest - one correlated predictor")
+print(rfImp2)
+
+
+simulated$duplicate2 = simulated$V1 + rnorm(200) * 0.1
+cor(simulated$duplicate2, simulated$V1)
+
+model3 = randomForest( y ~ ., data = simulated, importance = TRUE, ntree = 1000)
+rfImp3 = varImp(model3, scale = FALSE)
+rfImp3 = rfImp3[order(-rfImp3), , drop = FALSE]
+print("random forest - two correlated predictor")
+print(rfImp3)
+
+
+##--------------  QUESTION 8.4 (A-B) ----------------
+
+library(caret)
+library(AppliedPredictiveModeling)
+library(rpart)
+library(randomForest)
+
+set.seed(0)
+
+data(solubility)
+
+## solTrainX - training set predictors in natural units
+
+## solTrainXTrans - training set predictors after transformation for skewness and centering/scaling
+
+## solTrainY - vector of a log10 solubility values for the training set
+
+## solTestX - test set predictors in their natural units
+
+## solTestXtrans - test set predictors after the same transofrmations used on the training 
+                  ## set are applied
+
+## solTestY - a vector of log10 solubiliy values for the training set
+
+## we dont want to use the unscaled variables by accident so let's just remove them at the beginning
+
+rm(solTrainx)
+rm(solTestx)
+
+trainData = data.frame( x = solTrainXtrans$MolWeight, y = solTrainY)
+
+plot( trainData$x, trainData$y, xlab = 'MolWeight', ylab = 'log10(solubility')
+
+lmModel = lm(y ~ ., data = trainData)
+lm_yhat = predict(lmModel, newdata = data.frame(x = solTestXtrans$MolWeight) )
+plot ( solTestY, lm_yhat)
+
+
+### PART A - fit a simple regression tree
+
+rPartModel = rpart( y ~ ., data=trainData,
+                    method = "anova", 
+                    control = rpart.control(cp=0.01,maxdepth = 30))
+            ### decreasing cp makes deeper trees, increasing maxdepth
+
+plotcp(rPartModel)
+
+## plot the regression tree
+
+plot(rPartModel); text(rPartModel)
+
+
+rPart_yHat = predict(rPartModel, newdata = data.frame(x = solTestXtrans$MolWeight))
+plot(solTestXtrans$MolWeight, rPart_yHat, col = 'red', xlab = 'MolWeight',
+     ylab = 'log10(solubility)', main = 'rpart test set predict')
+lines(solTestXtrans$MolWeight, solTestY, type = 'p')
+
+## part B - fit a random forest
+
+rfModel = randomForest( y ~ ., data=trainData, ntree = 500) 
+
+# predict solubility
+
+rf_yHat = predict(rfModel, newdata = data.frame(x=solTestXtrans$MolWeight))
+
+plot( solTestXtrans$MolWeight, rf_yHat, col = 'red', xlab = 'MolWeight',
+      ylab = 'log10(solubility)', main = 'randomForest test set predictions')
+lines (solTestXtrans$MolWeight, solTestY, type = 'p')
+
+
+