3. Suppose we run an online news aggregation platform which observes a person\'s

Question

3. Suppose we run an online news aggregation platform which observes a person's habits of reading online news articles in order to recommend other articles the user might want to read. Suppose that we have characterised each online article by whether it mentions celebrity, whether it features sports, whether it features politics, and whether it features technology. Suppose we are given the examples below about whether a specific user reads various online articles. We want to use this data set to train a model that predicts which article this person would like to read based on the mentioned features. (a) Suppose you would like to use decision tree learning for this task. Apply the TDIDT algorithm to learn a decision tree. Stop extending a branch of the decision tree when all remaining training examples have the same target feature. Demonstrate how you compute the information gain for (7 marks) the features at each node. Draw the resulting decision tree. (b) Suppose you would like to use naive Bayesian learning for this task. Apply naive Bayesian learning algorithm to approximate P(Reads true), P(X | Reads-true), and P(X Reads false

Explanation / Answer

Only the Naive Bayes technique is illustrated here using the R software.

The data is first entered in a spreadsheet and the text is copied. Using the read.csv function, the data is imported in R. The R package e1071 is loaded and then the naiveBayes function is used to build the Laplace smoothing. For the test case, predict function is applied to make the prediction.

> reads <- read.csv("clipboard",sep=" ",header=TRUE)
> reads$Reads <- as.factor(reads$Reads)
> library(e1071)
> readNB <- naiveBayes(Reads~.,reads[,-1])
> predict(readNB,newdata=(list(Celebrity=FALSE,Sports=TRUE,Politics=FALSE,Technology=TRUE)))
[1] FALSE
Levels: FALSE TRUE
> reads
   Case Celebrity Sports Politics Technology Reads
1    E1     FALSE FALSE     TRUE       TRUE TRUE
2    E2      TRUE   TRUE    FALSE       TRUE FALSE
3    E3     FALSE FALSE    FALSE      FALSE FALSE
4    E4      TRUE FALSE    FALSE      FALSE TRUE
5    E5      TRUE FALSE    FALSE       TRUE FALSE
6    E6     FALSE   TRUE    FALSE      FALSE FALSE
7    E7     FALSE   TRUE     TRUE       TRUE TRUE
8    E8     FALSE   TRUE     TRUE      FALSE FALSE
9    E9      TRUE   TRUE     TRUE      FALSE TRUE
10 E10     FALSE FALSE     TRUE      FALSE FALSE
11 E11      TRUE   TRUE    FALSE      FALSE TRUE
12 E12      TRUE FALSE     TRUE      FALSE TRUE
13 E13     FALSE FALSE    FALSE       TRUE FALSE

Related Questions

Navigate

• Browse (All)
• Browse 3
• Subjects

---

---

Integrity-first tutoring: explanations and feedback only — we do not complete graded work. Learn more.