| output | github_document |
|---|
00002_example_1.1_of_section_1.2.4.R
# example 1.1 of section 1.2.4
# (example 1.1 of section 1.2.4) : The data science process : Stages of a data science project : Model evaluation and critique
# Title: Calculating the confusion matrix
library("rpart") # Note: 1
load("../Statlog/loan_model_example.RData") # Note: 2
conf_mat <- table(actual = d$Loan_status, pred = predict(model, type = 'class')) # Note: 3
conf_mat## pred
## actual BadLoan GoodLoan
## BadLoan 41 259
## GoodLoan 13 687
## pred
## actual BadLoan GoodLoan
## BadLoan 41 259
## GoodLoan 13 687
(accuracy <- sum(diag(conf_mat)) / sum(conf_mat)) # Note: 4 ## [1] 0.728
## [1] 0.728
(precision <- conf_mat["BadLoan", "BadLoan"] / sum(conf_mat[, "BadLoan"])) # Note: 5 ## [1] 0.7592593
## [1] 0.7592593
(recall <- conf_mat["BadLoan", "BadLoan"] / sum(conf_mat["BadLoan", ])) # Note: 6 ## [1] 0.1366667
## [1] 0.1366667
(fpr <- conf_mat["GoodLoan","BadLoan"] / sum(conf_mat["GoodLoan", ])) # Note: 7 ## [1] 0.01857143
## [1] 0.01857143
# Note 1:
# How to install all of the packages needed to
# run examples in this book can be found here:
# https://github.com/WinVector/PDSwR2/blob/master/packages.R
# Note 2:
# This file can be found at: https://github.com/WinVector/PDSwR2/tree/master/Statlog
# Note 3:
# Create the confusion matrix.
# Note 4:
# accuracy
# confusion matrix
# Overall model accuracy: 73% of the
# predictions were correct.
# Note 5:
# precision
# confusion matrix
# Model precision: 76% of the
# applicants predicted as bad really did
# default.
# Note 6:
# recall
# confusion matrix
# Model recall: the model found 14% of
# the defaulting loans.
# Note 7:
# false positive rate
# confusion matrix
# False positive rate: 2% of the good
# applicants were mistakenly identified as
# bad.