Assignment 5

Assignment 5.Rmd

@@ -8,28 +8,28 @@ For this assignment we will be using data from the Assistments Intelligent Tutor
 
 #Install & call libraries
 ```{r}
-install.packages("party", "rpart")
 
 library(rpart)
 library(party)
 ```
 
 ## Part I
 ```{r}
-D1 <- 
+D1 <- read.csv("~/Desktop/HUDK4050/Assignment 5/intelligent_tutor.csv")
 ```
 
 ##Classification Tree
 First we will build a classification tree to predict which students ask a teacher for help, which start a new session, or which give up, based on whether or not the student completed a session (D1$complete) and whether or not they asked for hints (D1$hint.y). 
 ```{r}
 
-c.tree <- rpart(action ~ hint.y + complete, method="class", data=D1) #Notice the standard R notion for a formula X ~ Y
+c.tree <- rpart(action ~ hint.y + complete, method = "class", data = D1) #Notice the standard R notion for a formula X ~ Y
 
 #Look at the error of this tree
 printcp(c.tree)
 
 #Plot the tree
-post(c.tree, file = "tree.ps", title = "Session Completion Action: 1 - Ask teacher, 2 - Start new session, 3 - Give up")
+post(c.tree, file = "", title = "Session Completion Action: 1 - Ask teacher, 2 - Start new session, 3 - Give up")
+
 
 ```
 ## Part II
@@ -41,41 +41,59 @@ We want to see if we can build a decision tree to help teachers decide which stu
 #Visualize our outcome variable "score"
 ```{r}
 
+score <- table(D1$score)
+
+barplot(score, xlab = "Score")
+
 ```
 
 #Create a categorical outcome variable based on student score to advise the teacher using an "ifelse" statement
 ```{r}
-D1$advice <- 
+D1$advice <- ifelse(D1$score > 0.70, 1, 0)
+
 ```
 
 #Build a decision tree that predicts "advice" based on how many problems students have answered before, the percentage of those problems they got correct and how many hints they required
 ```{r}
-score_ctree <- 
+advice_ctree <- rpart(advice ~ prior_prob_count + prior_percent_correct + hints, method="class", data = D1)
 ```
 
 #Plot tree
 ```{r}
+post(advice_ctree, file = "", title = "")
 
+summary(advice_ctree)
 ```
 
 Please interpret the tree, which two behaviors do you think the teacher should most closely pay attemtion to?
+# With 5 splits, the error is approximately 60%. The generalizability of this is 70%. Would recommend that the teacher
+# pay attention to the number of problems answered incorrectly by the student and the number of hints the student requested.
 
 #Test Tree
 Upload the data "intelligent_tutor_new.csv". This is a data set of a differnt sample of students doing the same problems in the same system. We can use the tree we built for the previous data set to try to predict the "advice" we should give the teacher about these new students. 
 
 ```{r}
 #Upload new data
 
-D2 <- 
+D2 <- read.csv("~/Desktop/HUDK4050/Assignment 5/intelligent_tutor_new.csv")
 
 #Generate predicted advice using the predict() command for new students based on tree generated from old students
 
-D2$prediction <- 
+predict(advice_ctree, D2)
+
+D2$prediction <- predict(advice_ctree, D2)
 
 ``` 
 ## Part III
 Compare the predicted advice with the actual advice that these students recieved. What is the difference between the observed and predicted results?
 
+```{r}
+summary(D2$prediction, D2$prior_percent_correct)
+
+```
+
+The measures of centrality seem to be way off. Other descriptive measures seem to be comparable.
+
 ### To Submit Your Assignment
 
 Please submit your assignment by first "knitting" your RMarkdown document into an html file and then commit, push and pull request both the RMarkdown file and the html file.

Assignment 5.Rproj

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