Created
June 21, 2019 21:22
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# Multinomial Logit Model vs Random Forest | |
# Predicting College Major from Items | |
library(data.table) # For fread function (v fast!) | |
library(dplyr) # For pre-processing | |
library(tm) # For cleaning text / pre-processing | |
library(nnet) #for MNL | |
library(randomForest) #For rf | |
# Helper function for making training / testing sets. | |
outersect <- function(x, y) { | |
sort(c(setdiff(x, y), | |
setdiff(y, x))) | |
} | |
# Normalizing majors (entered as free response data) | |
majors <- data.table::fread(input = paste(getwd(),'/data.csv',sep=''), sep = '\t',stringsAsFactors = F)$major %>% | |
toupper() %>% | |
trimws() %>% | |
removePunctuation() %>% | |
removeNumbers() %>% | |
table() %>% | |
melt() %>% | |
arrange(-value) %>% | |
.[-1,] | |
# Final data set | |
vi_data <- data.table::fread(input = paste(getwd(),'/data.csv',sep=''), sep = '\t',stringsAsFactors = F) %>% | |
mutate(MAJOR = toupper(major)) %>% | |
select(-major) %>% | |
filter(MAJOR %in% as.character(majors$.[2:10])) %>% | |
filter(!is.na(MAJOR)) %>% | |
select(R1:C8, MAJOR) %>% | |
mutate(R = rowSums(select(., grep("R[0-9]", names(.)))), | |
I = rowSums(select(., grep("I[0-9]", names(.)))), | |
A = rowSums(select(., grep("A[0-9]", names(.)))), | |
S = rowSums(select(., grep("S[0-9]", names(.)))), | |
E = rowSums(select(., grep("E[0-9]", names(.)))), | |
C = rowSums(select(., grep("C[0-9]", names(.))))) %>% | |
mutate(MAJOR = as.factor(MAJOR)) %>% | |
select(R,I,A,S,E,C,MAJOR) | |
test_rows <- sample(1:nrow(vi_data), size = 5000, replace = F) | |
train_rows <- outersect(x = 1:nrow(vi_data), y = test_rows) | |
mnl_vi <- multinom(MAJOR ~ R+I+A+S+E+C, data=vi_data[train_rows,]) | |
rf_vi <- randomForest::randomForest(MAJOR ~ ., data=vi_data[train_rows,]) | |
list( | |
`Multi-Nomial Logit` = data.frame(vi_data[test_rows,], | |
prediction = predict(mnl_vi, vi_data[test_rows,-ncol(vi_data)])) %>% | |
select(MAJOR, prediction) %>% | |
table(.) | |
, | |
`Random Forest` = data.frame(vi_data[test_rows,], | |
prediction = predict(rf_vi, vi_data[test_rows,-ncol(vi_data)])) %>% | |
select(MAJOR, prediction) %>% | |
table(.) | |
) |
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