Descriptive and diagnostic analytics describe and analyze organizational data through measures like mean, median, standard deviation and charts/tables to identify patterns and insights. Predictive analytics use statistical techniques like regression and clustering to predict future outcomes and behaviors. Prescriptive analytics provide recommendations for decisions and actions based on optimization models. The document then focuses on classification techniques in predictive analytics, covering methods like decision trees, rules, Naive Bayes and nearest neighbor algorithms.

1. Business
Analytics Using ‘R’

2. Types of Analytics

3. Types of Analytics Contd…

4. Descriptive and Diagnostic
Analytics
Function:
describe the main features of organizational data
Common tools:
sampling, mean, mode, median, standard deviation,
range, variance, stem and leaf diagram, histogram,
interquartile range, quartiles, and frequency distributions
Displaying results:
 graphics/charts, tables, and summary statistics such as
single numbers

5.  Function:
 draw conclusions and predict future behavior
 Common tools:
 cluster analysis, association analysis, multiple regression,
logistic regression, decision tree methods, neural
networks, text mining and forecasting tools (such as time
series and causal relationships)
Predictive Analytics

6. Function:
make decisions based on data
Common models:
linear programming
sensitivity analysis
integer programming
goal programming
nonlinear programming
simulation modeling
Prescriptive Analytics

7. Predictive
Analytics

8. Major Techniques
 Classification
Clustering
Association Mining
Regression

9. Classification

10. Classification Defined
The classification problem may be formalized
using a-posteriori probabilities:
 P(C|X) = prob. that the sample tuple
X=<x1,…,xk> is of class C.
E.g. P(class=N | outlook=sunny,windy=true,…)
Idea: assign to sample X the class label C such
that P(C|X) is maximal

11. Classification:
predicts categorical class labels
classifies data (constructs a model) based on the
training set and the values (class labels) in a
classifying attribute and uses it in classifying new data
Typical Applications
credit approval
target marketing
medical diagnosis
treatment effectiveness analysis
Classification

12. Classification—A Two-Step
Process
 Model construction: describing a set of predetermined
classes
Each tuple/sample is assumed to belong to a predefined class, as
determined by the class label attribute
The set of tuples used for model construction: training set
The model is represented as classification rules, decision trees, or
mathematical formulae
 Model usage: for classifying future or unknown objects
Estimate accuracy of the model
The known label of test sample is compared with the classified
result from the model
Accuracy rate is the percentage of test set samples that are
correctly classified by the model
Test set is independent of training set, otherwise over-fitting will
occur

13. Classification Process
(1): Model Construction
Training
Data
NAME RANK YEARS TENURED
Mike Assistant Prof 3 no
Mary Assistant Prof 7 yes
Bill Professor 2 yes
Jim Associate Prof 7 yes
Dave Assistant Prof 6 no
Anne Associate Prof 3 no
Classification
Algorithms
IF rank = ‘professor’
OR years > 6
THEN tenured = ‘yes’
Classifier
(Model)

14. Classification Process (2): Use
the Model in Prediction
Classifier
Testing
Data
NAME RANK YEARS TENURED
Tom Assistant Prof 2 no
Merlisa Associate Prof 7 no
George Professor 5 yes
Joseph Assistant Prof 7 yes
Unseen Data
(Jeff, Professor, 4)
Tenured?

15. Issues (1): Data Preparation
Data cleaning
Preprocess data in order to reduce noise and handle
missing values
Relevance analysis (feature selection)
Remove the irrelevant or redundant attributes
Data transformation
Generalize and/or normalize data

16. Issues (2): Evaluating
Classification Methods

17. Measurement
The Accuracy (AC) is the proportion of the
total number of predictions that were
correct.
Sensitivity or true positive rate (TP) is the
proportion of positive cases that were
correctly identified
Specificity : the proportion of actual
negative cases which are correctly
identified(TN).

18. Measurement Contd…

19. Decision Tree
Induction

20. Classification by Decision
Tree Induction
 Decision tree
A flow-chart-like tree structure
Internal node denotes a test on an attribute
Branch represents an outcome of the test
Leaf nodes represent class labels or class distribution
 Decision tree generation consists of two phases
Tree construction
At start, all the training examples are at the root
Partition examples recursively based on selected attributes
Tree pruning
Identify and remove branches that reflect noise or outliers
 Use of decision tree: Classifying an unknown sample
Test the attribute values of the sample against the decision tree

21. Training Dataset
age income student credit_rating
<=30 high no fair
<=30 high no excellent
31…40 high no fair
>40 medium no fair
>40 low yes fair
>40 low yes excellent
31…40 low yes excellent
<=30 medium no fair
<=30 low yes fair
>40 medium yes fair
<=30 medium yes excellent
31…40 medium no excellent
31…40 high yes fair
>40 medium no excellent
This
follows
an
example
from

22. Output: A Decision Tree for
“buys_computer”
age?
overcast
student? credit rating?
no yes fair
excellent
<=30 >40
no no
yes yes
yes
30..40

23. Extracting Classification
Rules from Trees
 Represent the knowledge in the form of IF-THEN rules
 One rule is created for each path from the root to a leaf
 Each attribute-value pair along a path forms a conjunction
 The leaf node holds the class prediction
 Rules are easier for humans to understand
 Example
IF age = “<=30” AND student = “no” THEN buys_computer = “no”
IF age = “<=30” AND student = “yes” THEN buys_computer = “yes”
IF age = “31…40” THEN buys_computer = “yes”
IF age = “>40” AND credit_rating = “excellent” THEN
buys_computer = “yes”
IF age = “>40” AND credit_rating = “fair” THEN buys_computer =
“no”

24. Bayesian Classification

25. Bayesian Theorem
Given training data D, posteriori probability of a
hypothesis h, P(h|D) follows the Bayes theorem
MAP (maximum posteriori) hypothesis
Practical difficulty: require initial knowledge of
many probabilities, significant computational
cost
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26. Bayesian classification
The classification problem may be formalized
using a-posteriori probabilities:
 P(C|X) = prob. that the sample tuple
X=<x1,…,xk> is of class C.
Idea: assign to sample X the class label C such
that P(C|X) is maximal

27. Naïve Bayesian
Classification
Naïve assumption: attribute independence
P(x1,…,xk|C) = P(x1|C)·…·P(xk|C)
If i-th attribute is categorical:
P(xi|C) is estimated as the relative freq of
samples having value xi as i-th attribute in class
C
If i-th attribute is continuous:
P(xi|C) is estimated thru a Gaussian density
function
Computationally easy in both cases

28. Play-tennis example
Outlook Temperature Humidity Windy Class
sunny hot high false N
sunny hot high true N
overcast hot high false P
rain mild high false P
rain cool normal false P
rain cool normal true N
overcast cool normal true P
sunny mild high false N
sunny cool normal false P
rain mild normal false P
sunny mild normal true P
overcast mild high true P
overcast hot normal false P
rain mild high true N
outlook
P(sunny|p) = 2/9 P(sunny|n) = 3/5
P(overcast|p) = 4/9 P(overcast|n) = 0
P(rain|p) = 3/9 P(rain|n) = 2/5
temperature
P(hot|p) = 2/9 P(hot|n) = 2/5
P(mild|p) = 4/9 P(mild|n) = 2/5
P(cool|p) = 3/9 P(cool|n) = 1/5
humidity
P(high|p) = 3/9 P(high|n) = 4/5
P(normal|p) = 6/9 P(normal|n) = 2/5
windy
P(true|p) = 3/9 P(true|n) = 3/5
P(false|p) = 6/9 P(false|n) = 2/5
P(p) = 9/14
P(n) = 5/14

29. Play-tennis example:
classifying X
An unseen sample X = <rain, hot, high, false>
P(X|p)·P(p) =
P(rain|p)·P(hot|p)·P(high|p)·P(false|p)·P(p) =
3/9·2/9·3/9·6/9·9/14 = 0.010582
P(X|n)·P(n) =
P(rain|n)·P(hot|n)·P(high|n)·P(false|n)·P(n) =
2/5·2/5·4/5·2/5·5/14 = 0.018286
Sample X is classified in class n (don’t play)

30. Other Classification
Methods
k-nearest neighbor classifier
case-based reasoning
Genetic algorithm
Rough set approach
Fuzzy set approaches
Support Vector Machine (SVM)
Logistic Regression