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rCh01.html
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<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="kw">source</span>(<span class="st">'runDir.R'</span>)</code></pre></div>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="kw">runDir</span>(<span class="st">'../CodeExamples/c01_The_data_science_process'</span>,
<span class="st">'../Statlog'</span>)</code></pre></div>
<pre><code>[1] "############################### start 1 Fri Jun 17 10:32:19 2016"
[1] "##### running ../CodeExamples/c01_The_data_science_process/00001_example_1.1_of_section_1.2.3.R"
[1] "##### in directory ../Statlog"
> # example 1.1 of section 1.2.3
> # (example 1.1 of section 1.2.3) : The data science process : Stages of a data science project : Modeling
> # Title: Building a decision tree
>
> library('rpart')
> load('GCDData.RData')
> model <- rpart(Good.Loan ~
Duration.in.month +
Installment.rate.in.percentage.of.disposable.income +
Credit.amount +
Other.installment.plans,
data=d,
control=rpart.control(maxdepth=4),
method="class")
[1] "############################### end 1 Fri Jun 17 10:32:20 2016"
[1] "############################### start 2 Fri Jun 17 10:32:20 2016"
[1] "##### running ../CodeExamples/c01_The_data_science_process/00002_example_1.2_of_section_1.2.4.R"
[1] "##### in directory ../Statlog"
> # example 1.2 of section 1.2.4
> # (example 1.2 of section 1.2.4) : The data science process : Stages of a data science project : Model evaluation and critique
> # Title: Plotting the confusion matrix
>
> creditdata <- d
> resultframe <- data.frame(Good.Loan=creditdata$Good.Loan,
pred=predict(model, type="class"))
> rtab <- table(resultframe) # Note: 1
> rtab
pred
Good.Loan BadLoan GoodLoan
BadLoan 41 259
GoodLoan 13 687
> ## pred
> ## Good.Loan BadLoan GoodLoan
> ## BadLoan 41 259
> ## GoodLoan 13 687
>
> sum(diag(rtab))/sum(rtab) # Note: 2
[1] 0.728
> ## [1] 0.728
> sum(rtab[1,1])/sum(rtab[,1]) # Note: 3
[1] 0.7592593
> ## [1] 0.7592593
> sum(rtab[1,1])/sum(rtab[1,]) # Note: 4
[1] 0.1366667
> ## [1] 0.1366667
> sum(rtab[2,1])/sum(rtab[2,]) # Note: 5
[1] 0.01857143
> ## [1] 0.01857143
>
> # Note 1:
> # Create the confusion matrix. Rows represent
> # actual loan status; columns represent predicted
> # loan status. The diagonal entries represent
> # correct predictions.
>
> # Note 2:
> # accuracyconfusion matrixOverall model accuracy: 73% of the predictions
> # were correct.
>
> # Note 3:
> # precisionconfusion matrixModel precision: 76% of the applicants
> # predicted as bad really did default.
>
> # Note 4:
> # recallconfusion matrixModel recall: the model found 14% of the
> # defaulting loans.
>
> # Note 5:
> # false positive rateconfusion matrixFalse positive rate: 2% of the good applicants
> # were mistakenly identified as bad.
>
[1] "############################### end 2 Fri Jun 17 10:32:20 2016"
[1] "############################### start 3 Fri Jun 17 10:32:20 2016"
[1] "##### running ../CodeExamples/c01_The_data_science_process/00003_example_1.3_of_section_1.3.1.R"
[1] "##### in directory ../Statlog"
> # example 1.3 of section 1.3.1
> # (example 1.3 of section 1.3.1) : The data science process : Setting expectations : Determining lower and upper bounds on model performance
> # Title: Plotting the relation between disposable income and loan outcome
>
> tab1 <- as.table(matrix(data=c(50,6,0,44),nrow=2,ncol=2))
> dimnames(tab1) <- list('loan.as.pct.disposable.income'=
c('LT.15pct','GT.15pct'),
'loan.quality.pop1'=
c('goodloan','badloan'))
> tab2 <- as.table(matrix(data=c(34,18,16,32),nrow=2,ncol=2))
> dimnames(tab2) <- list('loan.as.pct.disposable.income'=
c('LT.15pct','GT.15pct'),
'loan.quality.pop2'=
c('goodloan','badloan'))
> tab1
loan.quality.pop1
loan.as.pct.disposable.income goodloan badloan
LT.15pct 50 0
GT.15pct 6 44
> ## loan.quality.pop1 # Note: 1
> ## loan.as.pct.disposable.income goodloan badloan
> ## LT.15pct 50 0
> ## GT.15pct 6 44
> sum(diag(tab1))/sum(tab1) # Note: 2
[1] 0.94
> ## [1] 0.94
>
> tab2
loan.quality.pop2
loan.as.pct.disposable.income goodloan badloan
LT.15pct 34 16
GT.15pct 18 32
> ## loan.quality.pop2 # Note: 3
> ## loan.as.pct.disposable.income goodloan badloan
> ## LT.15pct 34 16
> ## GT.15pct 18 32
> sum(diag(tab2))/sum(tab2)
[1] 0.66
> ## [1] 0.66 # Note: 4
>
> # Note 1:
> # The count of correct predictions is on the
> # diagonal of tab1. In this first population, all
> # the loans that were less than 15% of disposable
> # income were good loans, and all but six of the
> # loans that were greater than 15% of disposable
> # income defaulted. So you know that
> # loan.as.pct.disposable.income models loan quality
> # well in this population. Or as statisticians might
> # say, loan.as.pct.disposable.income “explains” the
> # output (loan quality).
>
> # Note 2:
> # In fact, it’s 94% accurate.
>
> # Note 3:
> # In the second population, about a third of
> # the loans that were less than 15% of disposable
> # income defaulted, and over half of the loans that
> # were greater than 15% of disposable income were
> # good. So you know that
> # loan.as.pct.disposable.income doesn’t model loan
> # quality well in this population.
>
> # Note 4:
> # The rule of thumb is only 66%
> # accurate.
>
[1] "############################### end 3 Fri Jun 17 10:32:20 2016"</code></pre>