Naive Bayes R language

#### Table 8.4 install.packages('e1071') library(e1071) delays.df <- read.csv("FlightDelays.csv") # change numerical variables to categorical first delays.df$DAY_WEEK <- factor(delays.df$DAY_WEEK) delays.df$DEP_TIME <- factor(delays.df$DEP_TIME) # create hourly bins departure time delays.df$CRS_DEP_TIME <- factor(round(delays.df$CRS_DEP_TIME/100)) # Create training and validation sets. selected.var <- c(10, 1, 8, 4, 2, 13) train.index <- sample(c(1:dim(delays.df)[1]), dim(delays.df)[1]*0.6) train.df <- delays.df[train.index, selected.var] valid.df <- delays.df[-train.index, selected.var] # run naive bayes delays.nb <- naiveBayes(Flight.Status ~ ., data = train.df) delays.nb #### Table 8.5 # use prop.table() with margin = 1 to convert a count table to a proportion table, # where each row sums up to 1 (use margin = 2 for column sums). prop.table(table(train.df$Flight.Status, train.df$DEST), margin = 1) #### Table 8.6 ## predict probabilities pred.prob <- predict(delays.nb, newdata = valid.df, type = "raw") ## predict class membership pred.class <- predict(delays.nb, newdata = valid.df) df <- data.frame(actual = valid.df$Flight.Status, predicted = pred.class, pred.prob) df[valid.df$CARRIER == "DL" & valid.df$DAY_WEEK == 7 & valid.df$CRS_DEP_TIME == 10 & valid.df$DEST == "LGA" & valid.df$ORIGIN == "DCA",] #### Table 8.7 library(caret) # training pred.class <- predict(delays.nb, newdata = train.df) confusionMatrix(pred.class, train.df$Flight.Status) # validation pred.class <- predict(delays.nb, newdata = valid.df) confusionMatrix(pred.class, valid.df$Flight.Status) #### Figure 8.1 install.packages('gains') library(gains) gain <- gains(ifelse(valid.df$Flight.Status=="delayed",1,0), pred.prob[,1], groups=100) plot(c(0,gain$cume.pct.of.total*sum(valid.df$Flight.Status=="delayed"))~c(0,gain$cume.obs), xlab="# cases", ylab="Cumulative", main="", type="l") lines(c(0,sum(valid.df$Flight.Status=="delayed"))~c(0, dim(valid.df)[1]), lty=2)