LT-3 - Input: Concepts, instances, attributes Terminology...

Info iconThis preview shows pages 1–4. Sign up to view the full content.

View Full Document Right Arrow Icon
1 Input: Concepts, instances, attributes Terminology What’s a concept? Classification, association, clustering, numeric prediction What’s in an example? Relations, flat files What’s in an attribute? Nominal (categorical), ordinal (small, medium, large), interval, ratio Preparing the input Attributes, missing values, getting to know data 1 Terminology Components of the input: Concepts: kinds of things that can be learned Instances: the individual, independent examples of a concept Note: more complicated forms of input are possible Attributes: measuring aspects of an instance We will focus on nominal and numeric ones 2
Background image of page 1

Info iconThis preview has intentionally blurred sections. Sign up to view the full version.

View Full DocumentRight Arrow Icon
2 What’s a concept? Styles of learning: Classification learning: predicting a discrete class Association learning: detecting associations between features Clustering: grouping similar instances into clusters Numeric prediction: predicting a numeric quantity Concept: thing to be learned Concept description: output of learning scheme 3 Classification learning Example problems: weather data, heart disease, liver function data Classification learning is supervised Scheme is provided with actual outcome Outcome is called the class of the example Measure success on fresh data for which class labels are known ( test data ) 4
Background image of page 2
3 Association learning Can be applied if no class is specified and any kind of structure is considered “interesting” Difference to classification learning: Can predict any attribute’s value, not just the class Hence: far more association rules than classification rules 5 Clustering Finding groups of items that are similar Clustering is unsupervised The class of an example is not known Success often measured subjectively Sepal length Sepal width Petal length Petal width Type 1 5.1 3.5 1.4 0.2 Iris setosa 2 4.9 3.0 1.4 0.2 Iris setosa 51 7.0 3.2 4.7 1.4 Iris versicolor 52 6.4 3.2 4.5 1.5 Iris versicolor 101 6.3 3.3 6.0 2.5 Iris virginica 102 5.8 2.7 5.1 1.9 Iris virginica 6
Background image of page 3

Info iconThis preview has intentionally blurred sections. Sign up to view the full version.

View Full DocumentRight Arrow Icon
Image of page 4
This is the end of the preview. Sign up to access the rest of the document.

This note was uploaded on 12/06/2011 for the course DM 301 taught by Professor Dr.abdulazizgil during the Spring '11 term at American.

Page1 / 12

LT-3 - Input: Concepts, instances, attributes Terminology...

This preview shows document pages 1 - 4. Sign up to view the full document.

View Full Document Right Arrow Icon
Ask a homework question - tutors are online