Data Mining: A Closer Look. Chapter 2. 2.1 Data Mining Strategies (p35). Moh!. Classification. Learning is supervised. The dependent variable is categorical. Well-defined classes. Current rather than future behavior. Estimation. Learning is supervised.
Learning is supervised.
The dependent variable is categorical.
Current rather than future behavior.
Learning is supervised.
The dependent variable is numeric.
Well-defined output classes or variable.
Current rather than future behavior.(???)
The emphasis is on predicting future rather than current outcomes.
The output attribute may be categorical or numeric.
The output variable must correspond to the variable to be predicted (the dependent variable). The input variables are the predictor variables, (or independent variables).
Hence any supervised classification model, or supervised estimation model may be used for prediction if the variables are suitably chosen. That is:
if the output variable is “current” and
the input variables are previous attribute values
If you can classify/estimate the present from the past, then you can predict the future from the present!!!
Do We Know the Distribution of the Data?
Do We Know Which Attributes Best Define the Data?
Does the Data Contain Missing Values?
Is Time an Issue?
Which Technique Is Most Likely to Give a Best Test Set Accuracy?
Today we consider only the first three, which are machine learning based; the last three are statistically based.
High Heart Rate
IF 169 <= Maximum Heart Rate <=202
THEN Concept Class = Healthy
Rule accuracy: 85.07% High Heart rate is quite a good predictor of health
Rule coverage: 34.55% But there are other ways of being healthy.
Rule accuracy is a between-class measure.
Rule coverage is a within-class measure.
A Sick Class Rule for the Cardiology Patient Dataset
IF Thal = Rev & Chest Pain Type = Asymptomatic
THEN Concept Class = Sick
Rule accuracy: 91.14%
Rule coverage: 52.17%
Acceptance/rejection of the “Life Insurance Promotion” offer is the output variable.
A Hypothesis for the Insurance Promotion
For credit card holders,
A combination of one or more of the attributes
can differentiate those who say yes to the life insurance promotion
from those who say no.
c11 is the number with true class “1” which are correctly classified as class “1”
c12 is the number with true class “1” which are mis-classified as class “2”
A Simple Confusion Matrix
Figure 2.4 Targeted vs. mass mailing
The acceptance rate of those predicted to accept is 540/23,460 = 2.3%
The overall acceptance rate in the population is 1000/100,000
Therefore the lift in the response rate from using the classification model for targetted sampling/marketting is 2.3/1 = 2.3.
An Algorithm for Building Decision Trees
1. Let T be the set of training instances.2. Choose an attribute that best differentiates the instances in T.3. Create a tree node whose value is the chosen attribute. Create child links from this node where each link represents a unique value for the chosen attribute.Use the child link values to further subdivide the instances into subclasses.4. For each subclass created in step 3: If the instances in the subclass satisfy predefined criteria or if the set of remaining attribute choices for this path is null, specify the classification for new instances following this decision path. If the subclass does not satisfy the criteria and there is at least one attribute to further subdivide the path of the tree, let T be the current set of subclass instances and return to step 2.
Don’t worry too much about this. It is just “algorithm speak”, which we do not concern ourselves with.
3.1 Decision Trees
Rules for the Tree in Figure 3.4
IF Age <=43 & Sex = Male & Credit Card Insurance = NoTHEN Life Insurance Promotion = No
IF Sex = Female & 19 <=Age <= 43
THEN Life Insurance Promotion = Yes
Rule Accuracy: 100.00%
Rule Coverage: 66.67%
IF Sex = Male & Credit Card Insurance = No THEN Life Insurance Promotion = No
Easy to understand.
Map nicely to a set of production rules.
Applied to real problems.
Make no prior assumptions about the data.
Able to process both numerical and categorical data.
Output attribute must be categorical.
Limited to one output attribute.
Decision tree algorithms are unstable.
Trees created from numeric datasets can be complex.
4.1 The iData Analyzer
4.2 ESX: A Multipurpose Tool for Data Mining
A Live Demonstration
Step 1: Choose an Output Attribute
Step 2: Perform the Mining Session
Step 3: Read and Interpret Summary Results
Step 4: Read and Interpret Test Set Results
Step 5: Read and Interpret Class Results
Step 6: Visualize and Interpret Class Rules
Figure 4.12 Test set instance classification
Identify prototypical and outlier instances.
Select a best set of training instances.
Used to compute individual instance classification confidence scores.
CLASS SIMILARITY is the average similarity of members of a class with other members of the same class.
Given class C and categorical attribute A with values v1, v2,…vn, then the