304 Part II • Predictive Analytics/Machine Learning Introduction and Motivation Analytics has been used by many businesses, organi- zations, and government agencies to learn from past experiences to more effectively and efficiently use their limited resources to achieve their goals and objec- tives. Despite all the promises of analytics, however, its multidimensional and multidisciplinary nature can sometimes disserve its proper, full-fledged application. This is particularly true for the use of predictive analyt- ics in several social science disciplines because these domains are traditionally dominated by descriptive analytics (causal-explanatory statistical modeling) and might not have easy access to the set of skills required to build predictive analytics models. A review of the extant literature shows that drug court is one such area. While many researchers have studied this social phenomenon, its characteristics, its requirements, and its outcomes from a descriptive analytics perspective, there currently is a dearth of predictive analytics mod- els that can accurately and appropriately predict who would (or would not) graduate from intervention and treatment programs. To fill this gap and to help author- ities better manage the resources, and to improve the outcomes, this study sought to develop and compare several predictive analytics models (both single models and ensembles) to identify who would graduate from these treatment programs. Ten years after President Richard Nixon first declared a “war on drugs,” President Ronald Reagan signed an executive order leading to stricter drug enforcement, stating, “We’re taking down the surren- der flag that has flown over so many drug efforts; we are running up a battle flag.” The reinforcement of the war on drugs resulted in an unprecedented 10-fold surge in the number of citizens incarcerated for drug offences during the following two decades. The sky- rocketing number of drug cases inundated court dockets, overloaded the criminal justice system, and overcrowded prisons. The abundance of drug-related caseloads, aggravated by a longer processing time than that for most other felonies, imposed tremen- dous costs on state and federal departments of justice. Regarding the increased demand, court systems started to look for innovative ways to accelerate the inquest of drug-related cases. Perhaps analytics-driven deci- sion support systems are the solution to the problem. To support this claim, the current study’s goal was to build and compare several predictive models that use a large sample of data from drug courts across different locations to predict who is more likely to complete the treatment successfully. The researchers believed that this endeavor might reduce the costs to the criminal justice system and local communities. Methodology The methodology used in this research effort included a multi-step process that employed pre- dictive analytics.

1. 304 Part II • Predictive Analytics/Machine Learning
Introduction and Motivation
Analytics has been used by many businesses, organi-
zations, and government agencies to learn from past
experiences to more effectively and efficiently use
their limited resources to achieve their goals and objec-
tives. Despite all the promises of analytics, however,
its multidimensional and multidisciplinary nature can
sometimes disserve its proper, full-fledged application.
This is particularly true for the use of predictive analyt-
ics in several social science disciplines because these
domains are traditionally dominated by descriptive
analytics (causal-explanatory statistical modeling) and
might not have easy access to the set of skills required
to build predictive analytics models. A review of the
extant literature shows that drug court is one such
area. While many researchers have studied this social
phenomenon, its characteristics, its requirements, and
its outcomes from a descriptive analytics perspective,
there currently is a dearth of predictive analytics mod-
els that can accurately and appropriately predict who
would (or would not) graduate from intervention and
treatment programs. To fill this gap and to help author-
ities better manage the resources, and to improve the
outcomes, this study sought to develop and compare
several predictive analytics models (both single models
and ensembles) to identify who would graduate from
these treatment programs.
Ten years after President Richard Nixon first

2. declared a “war on drugs,” President Ronald Reagan
signed an executive order leading to stricter drug
enforcement, stating, “We’re taking down the surren-
der flag that has flown over so many drug efforts; we
are running up a battle flag.” The reinforcement of the
war on drugs resulted in an unprecedented 10-fold
surge in the number of citizens incarcerated for drug
offences during the following two decades. The sky-
rocketing number of drug cases inundated court
dockets, overloaded the criminal justice system, and
overcrowded prisons. The abundance of drug-related
caseloads, aggravated by a longer processing time
than that for most other felonies, imposed tremen-
dous costs on state and federal departments of justice.
Regarding the increased demand, court systems started
to look for innovative ways to accelerate the inquest
of drug-related cases. Perhaps analytics-driven deci-
sion support systems are the solution to the problem.
To support this claim, the current study’s goal was
to build and compare several predictive models that
use a large sample of data from drug courts across
different locations to predict who is more likely to
complete the treatment successfully. The researchers
believed that this endeavor might reduce the costs to
the criminal justice system and local communities.
Methodology
The methodology used in this research effort
included a multi-step process that employed pre-
dictive analytics methods in a social science con-
text. The first step of this process, which focused on
understanding the problem domain and the need to
conduct this study, was presented in the previous
section. For the steps of the process, the research-

3. ers employed a structured and systematic approach
to develop and evaluate a set of predictive models
using a large and feature-rich real-world data set.
The steps included data understanding, data pre-
processing, model building, and model evaluation;
they are reviewed in this section. The approach also
involved multiple iterations of experimentations and
numerous modifications to improve individual tasks
and to optimize the modeling parameters to achieve
the best possible outcomes. A pictorial depiction of
the methodology is given in Figure 5.25.
The Results
A summary of the models’ performances based on
accuracy, sensitivity, specificity, and AUC is pre-
sented in Table 5.10. As the results show, RF has
the best classification accuracy and the greatest AUC
among the models. The heterogeneous ensemble_
(HE) model closely follows RF, and SVM, ANN,
and LR rank third to last based on their classifica-
tion performances. RF also has the highest specific-
ity and the second highest sensitivity. Sensitivity in
the context of this study is an indicator of a model’s
ability in correctly predicting the outcome for suc-
cessfully graduated participants. Specificity, on the
other hand, determines how a model performs in
predicting the end results for those who do not suc-
cessfully complete the treatment. Consequently, it
can be concluded that RF outperforms other models
for the drug courts data set used in this study.
Application Case 5.6 To Imprison or Not to Imprison: A
Predictive Analytics-Based
Decision Support System for Drug Courts

4. Chapter 5 • Machine-Learning Techniques for Predictive
Analytics 305
10-fold
Cross-Validation
Data Preprocessing
Merging
Aggregating
Cleaning
Binning
Selecting
True
Positive
Count
(TP)
False
Positive
Count
(FP)
True
Negative
Count
(TN)
False
Negative
Count
(FN)
True/Observed Class

5. Positive Negative
P
os
it
iv
e
N
e
ga
ti
ve
P
re
di
c
te
d
C
la
s
s
100
90
80
70

6. 60
50
40
30
40
50
Variable Names
Im
po
rt
a
nc
e
X1
X2
ANN
LR
SVM
RF
Pre-processed
Data

7. SplittingData Preparation Modeling Assessment
Treat. DB Case DB
Court DB
Domain Expert(s)
X1
X2
M
ax
im
um
-m
ar
gi
n
hy
pe
rp
lan
e
M
argin
HE
Individual Models

8. Ensemble Models
10 %
10 %
10 %
10 %
10 %
10% 10%
10%
10%
10%
10%
10%
10%
10%
10%
Variable Importance
Prediction Accuracy
26 24 22 0
0.5
1

9. 2 4 6
26 24 22 0
0.5
1
2 4 6
FIGURE 5.25 Research Methodology Depicted as a Workflow.
TABLE 5.10 Performance of Predictive Models Using 10-Fold
Cross-Validation on the Balanced Data Set
Model
Type
Confusion Matrix Accuracy
(%)
Sensitivity
(%)
Specificity
(%) AUCG T
In
d
iv
id
u
a

10. l
M
o
d
e
ls ANN
G 6,831 1,072
86.63 86.76 86.49 0.909
T 1,042 6,861
SVM
G 6,911 992
88.67 89.63 87.75 0.917
T 799 7,104
LR
G 6,321 1,582
85.13 86.16 81.85 0.859
T 768 7,135
E
n
se
m
b
le
s

11. RF
G 6,998 905
91.16 93.44 89.12 0.927
T 491 7,412
HE
G 6,885 1,018
90.61 93.66 87.96 0.916
T 466 7,437
ANN: artificial neural networks; DT: decision trees; LR:
logistic regression; RF: random forest; HE: heterogeneous
ensemble; AUC: area under the
curve; G: graduated; T: terminated
(Continued )
306 Part II • Predictive Analytics/Machine Learning
u SECTION 5.9 REVIEW QUESTIONS
1. What is a model ensemble, and where can it be used
analytically?
2. What are the different types of model ensembles?
3. Why are ensembles gaining popularity over all other
machine-learning trends?
4. What is the difference between bagging- and boosting-type
ensemble models?
5. What are the advantages and disadvantages of ensemble
models?

12. Although the RF model performs better than the
other models in general, it falls second to the HE model
in the number of false negative predictions. Similarly,
the HE model has a slightly better performance in
true negative predictions. False positive predictions
represent participants who were terminated from the
treatment, but the models mistakenly classified them
as successful graduates. False negatives pertain to
individuals who graduated, but the models predicted
them as dropouts. False positive predictions are syn-
onymous with increased costs and opportunity losses
whereas false negatives carry social impacts. Spending
resources on those offenders who would recidivate at
some point in time during the treatment and, hence,
be terminated from the program prevented a number
of (potentially successful) prospective offenders from
participating in the treatment. Conspicuously, depriv-
ing potentially successful offenders from the treatment
is against the initial objective of drug courts in reinte-
grating nonviolent offenders into their communities.
In summary, traditional causal-explanatory sta-
tistical modeling, or descriptive analytics, uses sta-
tistical inference and significance levels to test and
evaluate the explanatory power of hypothesized
underlying models or to investigate the association
between variables retrospectively. Although a legiti-
mate approach for understanding the relationships
within the data used to build the model, descriptive
analytics falls short in predicting outcomes for pro-
spective observations. In other words, partial explana-
tory power does not imply predictive power, and
predictive analytics is a must for building empirical
models that predict well. Therefore, relying on the
findings of this study, application of predictive analyt-

13. ics (rather than the sole use of descriptive analytics) to
predict the outcomes of drug courts is well grounded.
Questions for Case 5.6
1. What are drug courts and what do they do for
the society?
2. What are the commonalities and differences
between traditional (theoretical) and modern
(machine-learning) base methods in studying
drug courts?
3. Can you think of other social situations and sys-
tems for which predictive analytics can be used?
Source: Zolbanin, H., and Delen, D. (2018). To Imprison or Not
to
Imprison: An Analytics-Based Decision Support System for
Drug
Courts. The Journal of Business Analytics (forthcoming).
Chapter Highlights
• Neural computing involves a set of methods
that emulates the way the human brain works.
The basic processing unit is a neuron. Multiple
neurons are grouped into layers and linked
together.
• There are differences between biological and arti-
ficial neural networks.
• In an artificial neural network, knowledge is
stored in the weight associated with each connec-
tion between two neurons.

14. • Neural network applications abound in almost
all business disciplines as well as in virtually all
other functional areas.
• Business applications of neural networks include
finance, bankruptcy prediction, time-series fore-
casting, and so on.
• There are various neural network architectures
for different types of problems.
• Neural network architectures can be applied not
only to prediction (classification or estimation)
Application Case 5.6 (Continued)