input - Input Concepts instances attributes CS 464...

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1 1 CS 464: Introduction to Machine Learning Input: Concepts, instances, attributes Slides for Chapter 2 adapted from http://www.cs.waikato.ac.nz/ml/weka/book.html 2 10/05/11 Input: Concepts, instances, attributes Terminology What’s a concept ? Classification, association, clustering, numeric prediction What’s in an example ? Relations, flat files, recursion What’s in an attribute ? Nominal, ordinal, interval, ratio Preparing the input ARFF, attributes, missing values, getting to know data 3 10/05/11 Terminology Components of the input: Concepts: kinds of things that can be learned Aim: intelligible and operational concept description 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 4 10/05/11 The weather problem c Conditions for playing a certain game 4 Yes False Normal Mild Rainy Yes False High Hot Overcast No True High Hot Sunny No False High Hot Sunny Play Windy Humidity Temperature Outlook If outlook = sunny and humidity = high then play = no If outlook = rainy and windy = true then play = no If outlook = overcast then play = yes If humidity = normal then play = yes If none of the above then play = yes 5 10/05/11 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 6 10/05/11 Classification learning Example problems: weather data, contact lenses, irises, labor negotiations 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 ) In practice success is often measured subjectively
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1 7 10/05/11 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, and more than one attribute’s value at a time Hence: far more association rules than classification rules Thus: constraints are necessary Minimum coverage and minimum accuracy 8 10/05/11 Clustering Finding groups of items that are similar Clustering is unsupervised The class of an example is not known Success often measured subjectively Iris virginica 1.9 5.1 2.7 5.8 102 101 52 51 2 1 Iris virginica 2.5 6.0 3.3 6.3 Iris versicolor 1.5 4.5 3.2 6.4 Iris versicolor 1.4 4.7 3.2 7.0 Iris setosa 0.2 1.4 3.0 4.9
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input - Input Concepts instances attributes CS 464...

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