random

Random Forest/RandomForest.R

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+#installing Pre-request Packages
+install.packages("stats")
+install.packages("dplyr")
+install.packages("randomForest")
+
+#load required libraries
+library(stats)
+library(dplyr)
+library(randomForest)
+
+#loading the Data Objects
+batsy = iris
+
+#inspect Data
+View(batsy)
+
+#Variable Selection
+str(batsy)
+
+#Splitting data in Training Process and as well as Testing
+#A Vector that has random sample of Training Values
+
+black=sample(2,nrow(batsy),replace = TRUE ,prob = c(0.65,0.35))
+
+#Let's do the Training
+Training = batsy[black==1,]
+
+#Testing Data
+Testing= batsy[black==2,]
+
+#Create Random Forest Model
+
+RF = randomForest(Species~.,data = Training)
+
+#Evaluating Model Accuracy
+Species_Pred = predict(RF,Testing)
+Testing$Species_Pred <- predict(RF,Testing)
+TestSpec_pred = Species_Pred 
+View(Testing)
+
+#Building Confusion Matrix
+CM = table(Testing$Species, Testing$Species_Pred)
+length(Testing$Species)
+#length(Testing$Species_Pred)
+CM
+
+classification_Accuracy = sum(diag(CM)/sum(CM))
+classification_Accuracy
+