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In this post, I examine a data set available in the UCI Machine Learning
repository: German Credit. This data set contains characteristics of
1000 bank clients, explanatory variables, and a variable indicating
whether the client has good or bad chances of obtaining a credit.
We will use Machine Learning models available in R package
learningmachine. The package (also available in
Python, click this link to see some examples)
still has a lot of ROUUUGH EDGES, and no docs, but this situation
will be vastly improved by the end of june 2024. This, hopefully, won’t
prevent you from understanding its general philosophy and how to use it.
Feel free to submit pull
requests.
Contents
0 – Install packages
utils::install.packages("caret", repos = "https://cloud.r-project.org")
The downloaded binary packages are in
/var/folders/cp/q8d6040n3m38d22z3hkk1zc40000gn/T//RtmpEReNQT/downloaded_packages
The downloaded binary packages are in
/var/folders/cp/q8d6040n3m38d22z3hkk1zc40000gn/T//RtmpEReNQT/downloaded_packages
The downloaded binary packages are in
/var/folders/cp/q8d6040n3m38d22z3hkk1zc40000gn/T//RtmpEReNQT/downloaded_packages
The downloaded binary packages are in
/var/folders/cp/q8d6040n3m38d22z3hkk1zc40000gn/T//RtmpEReNQT/downloaded_packages
german_credit <- read.table("http://archive.ics.uci.edu/ml/machine-learning-databases/statlog/german/german.data")
colnames(german_credit) <- c("chk_acct", "duration", "credit_his", "purpose",
"amount", "saving_acct", "present_emp", "installment_rate", "sex", "other_debtor",
"present_resid", "property", "age", "other_install", "housing", "n_credits",
"job", "n_people", "telephone", "foreign", "response")
german_credit$response <- german_credit$response - 1
german_credit$response[german_credit$response == 0] <- "bad"
german_credit$response[german_credit$response == 1] <- "good"
german_credit$response <- as.factor(german_credit$response)
print(table(german_credit$response)) # class imbalance, more bad credits, makes sense
bad good 700 300
1 – Data preparation
# obtain only numerical features X <- model.matrix.default(response ~ ., data = german_credit)[,-1] # explanatory variables y <- german_credit$response # target variable
set.seed(123) index_train <- caret::createDataPartition(y, p = 0.8)$Resample1 X_train <- X[index_train, ] # training set y_train <- y[index_train] # training set X_test <- X[-index_train, ] # test set y_test <- y[-index_train] # test set
2 Model training
With Random Forest
The nb_hidden parameter is the number of nodes in the hidden layer of
the neural network. This is inspired from Quasi-Randomized neural networks.
obj_rf <- learningmachine::Classifier$new(method = "ranger", nb_hidden = 25)
obj_rf$set_B(100L) # number of simulations for uncertainty estimation (buggy, fixed) obj_rf$set_level(95) # confidence level
t0 <- proc.time()[3]
obj_rf$fit(X_train, y_train, pi_method="kdesplitconformal") # this will be described in a paper or something similar, obtains simulations for probabilities
cat("Elapsed: ", proc.time()[3] - t0, "s \n")
Elapsed: 0.848 s
3 – Model predictions
probs <- obj_rf$predict_proba(X_test)
For individuals #158 and #171, we can plot the distribution of the
probability of having a good credit. This reads (among other possible
interpretations): with an error rate of 5% and an individual having the
same characteristics as #158, according to this specific model (a Random
Forest + quasi-random nodes in a layer), the probability of having a
good credit is comprised between:
customer_index1 <- 158 customer_index2 <- 171
t.test(probs$sims$good[158, ])$conf.int
[1] 0.4431368 0.5053498 attr(,"conf.level") [1] 0.95
For an individual having the same characteristics as #171, the
probability of having a good credit is comprised between (credit can be
granted):
t.test(probs$sims$good[171, ])$conf.int
[1] 0.6314353 0.7049742 attr(,"conf.level") [1] 0.95
df <- data.frame(id=c(customer_index1, customer_index2),
mean=c(mean(probs$sims$good[customer_index1,]),
mean(probs$sims$good[customer_index2,])),
lower=c(quantile(probs$sims$good[customer_index1,], 0.025),
quantile(probs$sims$good[customer_index2,], 0.025)),
upper=c(quantile(probs$sims$good[customer_index1,], 0.975),
quantile(probs$sims$good[customer_index2,], 0.975)))
ggplot2::ggplot(df, aes(x=id, y=mean)) +
geom_point(size = 4) +
geom_errorbar(aes(ymin=lower, ymax=upper), width=.05) +
labs(title="Probability of having a good credit for individual #36 and #151", x="Individual", y="Probability") +
theme_minimal()
This can be verified for these 2 individuals by looking at the actual
response:
print(y_test[c(customer_index1, customer_index2)])
[1] bad good Levels: bad good
print(X_test[c(customer_index1, customer_index2), ])
chk_acctA12 chk_acctA13 chk_acctA14 duration credit_hisA31 credit_hisA32
770 0 0 1 12 0 0
815 0 0 0 48 0 1
credit_hisA33 credit_hisA34 purposeA41 purposeA410 purposeA42 purposeA43
770 0 1 0 0 0 1
815 0 0 0 0 0 0
purposeA44 purposeA45 purposeA46 purposeA48 purposeA49 amount
770 0 0 0 0 0 1655
815 0 0 0 0 0 3931
saving_acctA62 saving_acctA63 saving_acctA64 saving_acctA65 present_empA72
770 0 0 0 0 0
815 0 0 0 0 0
present_empA73 present_empA74 present_empA75 installment_rate sexA92 sexA93
770 0 0 1 2 0 1
815 0 1 0 4 0 1
sexA94 other_debtorA102 other_debtorA103 present_resid propertyA122
770 0 0 0 4 0
815 0 0 0 4 0
propertyA123 propertyA124 age other_installA142 other_installA143
770 0 0 63 0 1
815 0 1 46 0 1
housingA152 housingA153 n_credits jobA172 jobA173 jobA174 n_people
770 1 0 2 1 0 0 1
815 0 1 1 0 1 0 2
telephoneA192 foreignA202
770 1 0
815 0 0
Other insights can be obtained by looking at the sensitivity of these
probabilities to a small change in the input features:
obj_rf$summary(X_test, y_test, class_name = "good")
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|======================================================================| 100%$ttests
estimate lower upper p-value signif
chk_acctA12 2.866628e-01 -0.1928077200 7.661333e-01 2.398135e-01
chk_acctA13 2.378946e-01 -0.4616138353 9.374030e-01 5.032280e-01
chk_acctA14 8.315866e-02 -0.4235356624 5.898530e-01 7.465524e-01
duration 9.503593e-03 0.0065260625 1.248112e-02 1.934730e-09 ***
credit_hisA31 -1.463447e-01 -0.8962360480 6.035466e-01 7.007696e-01
credit_hisA32 -6.963395e-02 -0.2858254135 1.465575e-01 5.260569e-01
credit_hisA33 5.375104e-01 0.0196516923 1.055369e+00 4.199342e-02 *
credit_hisA34 1.065668e-01 -0.4428390009 6.559726e-01 7.025022e-01
purposeA41 1.492564e-01 -0.5592482065 8.577610e-01 6.782819e-01
purposeA410 7.286522e-02 -1.1776013520 1.323332e+00 9.086349e-01
purposeA42 6.336391e-02 -0.5280075152 6.547353e-01 8.328772e-01
purposeA43 -5.145645e-01 -1.0662239242 3.709486e-02 6.735248e-02 .
purposeA44 1.830964e-02 -1.1012994662 1.137919e+00 9.743061e-01
purposeA45 -2.082171e-01 -1.0634041966 6.469700e-01 6.316672e-01
purposeA46 1.607812e-01 -0.6056958203 9.272581e-01 6.795756e-01
purposeA48 1.934510e-01 -1.0192745472 1.406177e+00 7.534241e-01
purposeA49 1.141721e-01 -0.6032982799 8.316424e-01 7.540015e-01
amount -9.233378e-06 -0.0000241999 5.733142e-06 2.252088e-01
saving_acctA62 -1.791712e-02 -0.4529104828 4.170762e-01 9.353457e-01
saving_acctA63 -2.877816e-01 -0.9103934331 3.348303e-01 3.631500e-01
saving_acctA64 -5.484981e-02 -0.6988110092 5.891114e-01 8.667832e-01
saving_acctA65 -2.320988e-01 -0.7750382076 3.108405e-01 4.002499e-01
present_empA72 -4.288099e-02 -0.6115880875 5.258261e-01 8.819511e-01
present_empA73 -2.535393e-02 -0.4317895531 3.810817e-01 9.022210e-01
present_empA74 1.065930e-01 -0.4069873095 6.201734e-01 6.827762e-01
present_empA75 -4.092760e-02 -0.5363583786 4.545032e-01 8.707597e-01
installment_rate 3.046334e-02 0.0127585556 4.816812e-02 8.340803e-04 ***
sexA92 1.322121e-01 -0.0971965450 3.616208e-01 2.571257e-01
sexA93 -1.702534e-01 -0.5588612709 2.183544e-01 3.886630e-01
sexA94 -8.904505e-01 -1.5169988038 -2.639021e-01 5.571408e-03 **
other_debtorA102 -1.703366e-01 -0.9532761016 6.126030e-01 6.683731e-01
other_debtorA103 2.025036e-01 -0.6345644743 1.039572e+00 6.338458e-01
present_resid 3.757648e-02 0.0186243133 5.652864e-02 1.266420e-04 ***
propertyA122 3.373646e-01 -0.0364643491 7.111936e-01 7.666657e-02 .
propertyA123 1.935712e-01 -0.3518870858 7.390296e-01 4.848668e-01
propertyA124 3.098673e-01 -0.2204829963 8.402176e-01 2.506403e-01
age -7.948128e-03 -0.0102349661 -5.661289e-03 8.815565e-11 ***
other_installA142 3.706325e-02 -0.6727217143 7.468482e-01 9.180898e-01
other_installA143 1.260310e-01 -0.0735411150 3.256031e-01 2.144849e-01
housingA152 -3.180369e-01 -0.6359422652 -1.315398e-04 4.990619e-02 *
housingA153 -5.257518e-01 -1.1228101597 7.130662e-02 8.403259e-02 .
n_credits 4.199850e-02 0.0090041088 7.499289e-02 1.286638e-02 *
jobA172 -6.004859e-01 -1.1685872713 -3.238462e-02 3.840312e-02 *
jobA173 1.502447e-01 -0.1516701848 4.521596e-01 3.276250e-01
jobA174 -7.947721e-02 -0.7201773080 5.612229e-01 8.070057e-01
n_people -2.361480e-02 -0.0750490125 2.781941e-02 3.663605e-01
telephoneA192 -5.883641e-02 -0.3758199606 2.581471e-01 7.147377e-01
foreignA202 -1.297824e-01 -0.8829349965 6.233702e-01 7.343615e-01
$effects
── Data Summary ────────────────────────
Values
Name effects
Number of rows 200
Number of columns 48
_______________________
Column type frequency:
numeric 48
________________________
Group variables None
── Variable type: numeric ──────────────────────────────────────────────────────
skim_variable mean sd p0 p25 p50
1 chk_acctA12 0.287 3.44 -19.6 0 0
2 chk_acctA13 0.238 5.02 -19.9 0 0
3 chk_acctA14 0.0832 3.63 -19.6 -0.0115 0
4 duration 0.00950 0.0214 -0.0536 -0.00187 0.00614
5 credit_hisA31 -0.146 5.38 -35.3 0 0
6 credit_hisA32 -0.0696 1.55 -21.6 0 0
7 credit_hisA33 0.538 3.71 -13.6 0 0
8 credit_hisA34 0.107 3.94 -30.7 0 0
9 purposeA41 0.149 5.08 -22.4 0 0
10 purposeA410 0.0729 8.97 -37.6 0 0
11 purposeA42 0.0634 4.24 -35.3 0 0
12 purposeA43 -0.515 3.96 -30.7 0 0
13 purposeA44 0.0183 8.03 -29.9 0 0
14 purposeA45 -0.208 6.13 -35.9 0 0
15 purposeA46 0.161 5.50 -30.7 0 0
16 purposeA48 0.193 8.70 -32.2 0 0
17 purposeA49 0.114 5.15 -30.7 0 0
18 amount -0.00000923 0.000107 -0.000527 -0.0000665 0.00000537
19 saving_acctA62 -0.0179 3.12 -21.6 0 0
20 saving_acctA63 -0.288 4.47 -23.1 0 0
21 saving_acctA64 -0.0548 4.62 -22.5 0 0
22 saving_acctA65 -0.232 3.89 -30.7 0 0
23 present_empA72 -0.0429 4.08 -30.7 0 0
24 present_empA73 -0.0254 2.91 -19.9 0 0
25 present_empA74 0.107 3.68 -19.9 0 0
26 present_empA75 -0.0409 3.55 -19.6 0 0
27 installment_rate 0.0305 0.127 -0.312 -0.0500 0.0252
28 sexA92 0.132 1.65 -0.823 0 0
29 sexA93 -0.170 2.79 -19.9 -0.0552 0
30 sexA94 -0.890 4.49 -30.7 0 0
31 other_debtorA102 -0.170 5.61 -22.4 0 0
32 other_debtorA103 0.203 6.00 -23.1 0 0
33 present_resid 0.0376 0.136 -0.337 -0.0376 0.0328
34 propertyA122 0.337 2.68 -6.81 0 0
35 propertyA123 0.194 3.91 -30.7 0 0
36 propertyA124 0.310 3.80 -23.1 0 0
37 age -0.00795 0.0164 -0.0691 -0.0152 -0.00689
38 other_installA142 0.0371 5.09 -35.3 0 0
39 other_installA143 0.126 1.43 -1.20 -0.0902 0
40 housingA152 -0.318 2.28 -19.6 -0.101 0
41 housingA153 -0.526 4.28 -30.7 0 0
42 n_credits 0.0420 0.237 -0.764 -0.0953 0.0332
43 jobA172 -0.600 4.07 -35.3 0 0
44 jobA173 0.150 2.17 -6.81 -0.159 0
45 jobA174 -0.0795 4.59 -23.1 0 0
46 n_people -0.0236 0.369 -1.78 -0.203 -0.0173
47 telephoneA192 -0.0588 2.27 -19.6 0 0
48 foreignA202 -0.130 5.40 -35.3 0 0
p75 p100 hist
1 0 30.7 ▁▇▁▁▁
2 0 35.3 ▁▇▁▁▁
3 0 23.1 ▁▁▇▁▁
4 0.0184 0.115 ▁▇▃▁▁
5 0 22.4 ▁▁▁▇▁
6 0.0753 1.39 ▁▁▁▁▇
7 0 21.7 ▁▇▁▁▁
8 0 21.7 ▁▁▇▁▁
9 0 35.3 ▁▇▁▁▁
10 0 35.3 ▁▁▇▁▁
11 0 19.9 ▁▁▁▇▁
12 0 15.5 ▁▁▁▇▁
13 0 30.7 ▁▁▇▁▁
14 0 30.7 ▁▁▇▁▁
15 0 23.1 ▁▁▇▁▁
16 0 41.0 ▁▁▇▁▁
17 0 35.3 ▁▁▇▁▁
18 0.0000579 0.000229 ▁▁▃▇▃
19 0 30.7 ▁▁▇▁▁
20 0 21.7 ▁▁▇▁▁
21 0 22.4 ▁▁▇▁▁
22 0 21.6 ▁▁▇▁▁
23 0 35.3 ▁▁▇▁▁
24 0 23.1 ▁▁▇▁▁
25 0 23.1 ▁▁▇▁▁
26 0 21.7 ▁▁▇▁▁
27 0.0936 0.598 ▁▇▅▁▁
28 0 23.1 ▇▁▁▁▁
29 0.0324 19.2 ▁▁▇▁▁
30 0 13.2 ▁▁▁▇▁
31 0 23.1 ▁▁▇▁▁
32 0 35.9 ▁▇▁▁▁
33 0.110 0.680 ▁▇▅▁▁
34 0 23.1 ▁▇▁▁▁
35 0 23.1 ▁▁▇▁▁
36 0 21.6 ▁▁▇▁▁
37 0.00195 0.0540 ▁▂▇▂▁
38 0 30.7 ▁▁▇▁▁
39 0.174 19.9 ▇▁▁▁▁
40 0.0725 0.851 ▁▁▁▁▇
41 0 21.6 ▁▁▇▁▁
42 0.161 0.879 ▁▃▇▂▁
43 0 13.6 ▁▁▁▇▁
44 0.00207 19.9 ▁▇▁▁▁
45 0 35.3 ▁▇▁▁▁
46 0.174 1.32 ▁▁▇▅▁
47 0 19.9 ▁▁▇▁▁
48 0 30.7 ▁▁▇▁▁
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