Classification : Basic Concepts

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Classification : Basic

ConceptsLecture 10


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Classification:

predicts categorical class labels

Most suited for predicting/ describing data

sets with binary or nominal categories.

Less effective for ordinal categories. Supervised learning


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Examples of Classification 1.Predicting tumor cells as benign or

malignant

2. Classifying credit card transactions as

legitimate or fraudulent

3. Classifying secondary structures of protein

as alpha-helix, beta-sheet or random coil

4. Categorizing news stories as finance,weather, entertainment, sports etc


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Examples Contd 5. Determine those characteristics that differentiate

individuals who have suffered a heart attack from thosewho have not suffered.

6. Develop a profile of a successful man.

7. Classifying galaxies based on their shapes.

8. Detecting spam emails based on their message

header and content.


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Classification: Definition

Given a collection of records (training set)

Each record contains a set ofattributes, one of the attributes is the class label.

Find a model for class attribute as a function of the values of otherattributes.

Goal: previously unseen records should be assigned a class asaccurately as possible.

A test setis used to determine the accuracy of the model. Usually, the givendata set is divided into training and test sets, with training set used to build themodel and test set used to validate it.


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Illustrating Classification Task

Apply

Model

Induction

Deduction

Learn

Model

Tid Attrib1 Attrib2 Attrib3 Class

1 Yes Large 125K No

2 No Medium 100K No

3 No Small 70K No

4 Yes Medium 120K No

5 No Large 95K Yes

6 No Medium 60K No

7 Yes Large 220K No

8 No Small 85K Yes

9 No Medium 75K No

10 No Small 90K Yes10

Tid Attrib1 Attrib2 Attrib3 Class

11 No Small 55K ?

12 Yes Medium 80K ?

13 Yes Large 110K ?

14 No Small 95K ?

Learning

algorithm

Training Set


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Definition Is a task of learning a target function f

(classification model) that maps each attribute set

x to one of the predefined class label y. Each record is characterized by a tuple (x,y) where xis the attribute set and y is special attribute (classlabel)

Output attributes are also known as dependent

variables Input attributes are termed as independent variables Classification can be categorized based on whether

output variable is discrete/categorical. Or whether models are designed fora current

condition/predicting future outcomes.


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The vertebrate data setName Body temp Skin Cover Gives birth Aquatic creature Legs Hibernates Class label

human warm hair y n y n mammal

python cold scales n n n y reptile

salmon cold scales n yes n n fish

whale warm hair y yes n n mammal

frog cold none n semi y y amphibian

komodo cold scales n n y n reptile

dragon

bat warm hair y n y y mammal

pigeon warm feathers n n y n bird

cat warm fur y n y n mammal

leopard cold scales y yes y n fish

shark

turtle cold scales n semi y n reptile

penguin warm feathers n semi y n bird


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A Classification model is useful for the

following purposes:

Descriptive Modeling

Predictive Modeling


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Descriptive Modeling: A classification model canbe used as an explanatory tool to differentiatebetween objects of different classes. Examples: (1)A bank loan officer wants to analyze the

data regarding the loans applications assafe or risky for the bank.

Here data analysis task is CLASSIFICATION,where a model or classifier is constructed topredict categorical labels such as safe orrisky


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The vertebrate datasetName Body temp Skin Cover Gives birth Aquatic creature Legs Hibernates Class label

human warm hair y n y n mammal

python cold scales n n n y reptile

salmon cold scales n yes n n fish

whale warm hair y yes n n mammal

frog cold none n semi y y amphibian

komodo cold scales n n y n reptile

dragon

bat warm hair y n y y mammal

pigeon warm feathers n n y n bird

cat warm fur y n y n mammal

leopard cold scales y yes y n fish

shark

turtle cold scales n semi y n reptile

penguin warm feathers n semi y n bird


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Predictive Modeling: A classification model can beused to predict the class label of unknown records.

Example

(1) Suppose a Marketing Manager wants to estimatethe amount that a customer will spend during anongoing sale.

This is an example data analysis of numeric

prediction. Here the model (predictor) so constructed predicts a

continuous-valued function.


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(2) A medical researcher wants to analyze

brain tumour data to predict which particular

type of treatment say A, B or C is to be given

to the patient. Treatment A, Treatment B, or Treatment

C in this case, is classification task

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Classification : Basic Concepts