Updated Assignment 6 - Hsiao-Ting Yang

.gitignore

@@ -0,0 +1,4 @@
+.Rproj.user
+.Rhistory
+.RData
+.Ruserdata

Assignment6.Rmd

@@ -1,6 +1,10 @@
 ---
 title: "Assignment 6"
-author: "Charles Lang"
+<<<<<<< HEAD
+author: "Charles Lang, Edited by Hsiao Yang" 
+=======
+author: "Charles Lang" Edited by Hsiao Yang
+>>>>>>> 78a66c218b82b823759c202e5ed3dcd0a4bfb079
 date: "11/16/2016"
 output: html_document
 ---
@@ -18,32 +22,44 @@ assignment (numeric) - A student's average grade for the course assignments
 #Packages
 ```{r}
 library(rpart)
+<<<<<<< HEAD
+# install.packages("
+library(tidyverse)
+=======
+install.packages("rpart.plot")
+>>>>>>> 78a66c218b82b823759c202e5ed3dcd0a4bfb079
+library(rpart.plot)
 ```
 
 #Data
 ```{r}
 #Upload the data sets MOOC1.csv and MOOC2.csv
 M1 <- read.csv("MOOC1.csv", header = TRUE)
 
-M2 <- 
+M2 <- read.csv("MOOC2.csv", header = TRUE)
 
 ```
 
 #Decision tree
 ```{r}
 #Using the rpart package generate a classification tree predicting certified from the other variables in the M1 data frame. Which variables should you use?
 
-c.tree1 <- 
+c.tree1 <- rpart(as.factor(certified) ~ grade + assignment, method = "class", data = M1)
 
 #Check the results from the classifcation tree using the printcp() command
 
-
+printcp(c.tree1)
 
 #Plot your tree
 
-post(c.tree1, file = "tree1.ps", title = "MOOC") #This creates a pdf image of the tree
+rpart.plot(c.tree1)
+post(c.tree1, file = "tree1.ps", title = "MOOC") 
+
+#This creates a pdf image of the tree
 
 ```
+##Comment
+Variables that we will include is grade and assignment so we can prune the tree. If we include forum posts, there is only two nodes so then we will not be able to prune the tree for part II of this assignment.
 
 ##Part II
 
@@ -52,10 +68,13 @@ post(c.tree1, file = "tree1.ps", title = "MOOC") #This creates a pdf image of th
 #If we are worried about overfitting we can remove nodes form our tree using the prune() command, setting cp to the CP value from the table that corresponds to the number of nodes we want the tree to terminate at. Let's set it to two nodes.
 
 ```{r}
-c.tree2 <- prune(c.tree1, cp = )#Set cp to the level at which you want the tree to end
+c.tree2 <- prune(c.tree1, cp = 0.058182 )#Set cp to the level at which you want the tree to end
 
 #Visualize this tree and compare it to the one you generated earlier
 
+printcp(c.tree2)
+rpart.plot(c.tree2)
+
 post(c.tree2, file = "tree2.ps", title = "MOOC") #This creates a pdf image of the tree
 ```
 
@@ -66,19 +85,85 @@ M2$predict1 <- predict(c.tree1, M2, type = "class")
 
 M2$predict2 <- predict(c.tree2, M2, type = "class")
 
-table(M2$certified, M2$predict1)
+T1 <- table(M2$certified, M2$predict1)
+
+T2 <- table(M2$certified, M2$predict2)
+<<<<<<< HEAD
+
+#Tree 1 True Results
+T1TR <- (T1[2,2]+T1[1,1])/sum(T1)
+
+=======
 
-table(M2$certified, M2$predict2)
+#Tree 1 True Results
+T1TR <- (T1[2,2]+T1[1,1])/sum(T1)
+
+>>>>>>> 78a66c218b82b823759c202e5ed3dcd0a4bfb079
+#Tree 2 True Results
+T2TR <- (T2[2,2]+T2[1,1])/sum(T2)
 
 ```
+#Comment
+The second tree has a lower error rate. This is because the True Negative and True Positive for tree 2 is 53.63% while tree 1 is 21.86% so that means the second tree has a higher accuracy vesus the first tree.
 
 ##Part III
 
 Choose a data file from the (University of Michigan Open Data Set)[https://github.com/bkoester/PLA/tree/master/data]. Choose an outcome variable that you would like to predict. Build two models that predict that outcome from the other variables. The first model should use raw variables, the second should feature select or feature extract variables from the data. Which model is better according to the cross validation metrics?
 
 ```{r}
 
+<<<<<<< HEAD
+R.pre <- read.csv("student.record.csv", header = TRUE)
+R <- R.pre %>% 
+  filter(HSGPA <= 4)
+
+c.tree3 <- rpart(HSGPA ~ ., data = R)
+
+printcp(c.tree3)
+
+R$predict1 <- predict(c.tree3, R)
+
+T1 <- R %>% 
+  select(HSGPA, predict1) %>% 
+  mutate(
+    residual = HSGPA - predict1,
+    squared.residual = residual^2
+  )
+
+sqrt(mean(T1$squared.residual, na.rm = T))
+
+c.tree3$variable.importance
+R.select <- R %>% 
+  select(HSGPA, ADMIT_TERM, MAJOR1_DESCR, MAJOR1_DEPT)
+
+c.tree4 <- rpart(HSGPA ~ ., data = R.select)
+
+R.select$predict1 <- predict(c.tree4, R.select)
+
+T2 <- R.select %>% 
+  select(HSGPA, predict1) %>% 
+  mutate(
+    residual = HSGPA - predict1,
+    squared.residual = residual^2
+  )
+
+sqrt(mean(T2$squared.residual, na.rm = T))
+
+
+=======
+R <- read.csv("student.record.csv", header = TRUE)
+
+c.tree3 <- rpart(as.factor(HSGPA) ~., method = "class", data = R)
+
+printcp(c.tree3)
+
+>>>>>>> 78a66c218b82b823759c202e5ed3dcd0a4bfb079
 ```
+##Comment
+First model prediction = 1.322
+Second model prediction = 1.296
+
+The second model that selected Admit_Term, Major1_Descr, and Major1_Dept has a better prediction since the prediction is lower than the first model, which is closer to 0.
 
 
 ### To Submit Your Assignment

assignment6.Rproj

@@ -0,0 +1,13 @@
+Version: 1.0
+
+RestoreWorkspace: Default
+SaveWorkspace: Default
+AlwaysSaveHistory: Default
+
+EnableCodeIndexing: Yes
+UseSpacesForTab: Yes
+NumSpacesForTab: 2
+Encoding: UTF-8
+
+RnwWeave: Sweave
+LaTeX: pdfLaTeX