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Identifying risky bank loans using C5.0 with boosting and cost matrix
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# Download data set via: | |
# http://archive.ics.uci.edu/ml/datasets/Statlog+%28German+Credit+Data%29 | |
# | |
# Note, the example below uses the pre-processed data that is used in the book 'Machine Learning with R' by Brett Lantz | |
library(C50) | |
df <- read.csv("credit.csv", stringsAsFactors=TRUE) | |
set.seed(12345) | |
df_rand <- df[order(runif(1000)),] | |
df_train <- df_rand[1:900,] | |
df_test <- df_rand[901:1000,] | |
names <- list(c("no", "yes"), c("no", "yes")) | |
error_cost <- matrix(c(0,1,4,0), nrow=2, dimnames=names) | |
df_model <- C5.0(df_train[-17], df_train$default, trials = 10, costs = error_cost) | |
df_pred <- predict(df_model, df_test) | |
confusionMatrix(df_pred, df_test$default) |
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