Data Mining with Weka
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1 Data Mining with Weka Class 1 Lesson 1 Introduction Ian H. Witten Department of Computer Science University of Waikato New Zealand weka.waikato.ac.nz
2 Data Mining with Weka a practical course on how to use Weka for data mining explains the basic principles of several popular algorithms Ian H. Witten University of Waikato, New Zealand 2
3 Data Mining with Weka What s data mining? We are overwhelmed with data Data mining is about going from data to information, information that can give you useful predictions Examples?? You re at the supermarket checkout. You re happy with your bargains and the supermarket is happy you ve bought some more stuff Say you want a child, but you and your partner can t have one. Can data mining help? Data mining vs. machine learning 3
4 Data Mining with Weka What s Weka? A bird found only in New Zealand? Data mining workbench Waikato Environment for Knowledge Analysis Machine learning algorithms for data mining tasks 100+ algorithms for classification 75 for data preprocessing 25 to assist with feature selection 20 for clustering, finding association rules, etc 4
5 Data Mining with Weka What will you learn? Load data into Weka and look at it Use filters to preprocess it Explore it using interactive visualization Apply classification algorithms Interpret the output Understand evaluation methods and their implications Understand various representations for models Explain how popular machine learning algorithms work Be aware of common pitfalls with data mining Use Weka on your own data and understand what you are doing! 5
6 Class 1: Getting started with Weka Install Weka Explore the Explorer interface Explore some datasets Build a classifier Interpret the output Use filters Visualize your data set 6
7 Course organization Class 1 Getting started with Weka Class 2 Evaluation Class 3 Simple classifiers Class 4 More classifiers Class 5 Putting it all together Lesson 1.1 Lesson 1.2 Lesson 1.3 Lesson 1.4 Lesson 1.5 Lesson 1.6 Activity 1 Activity 2 Activity 3 Activity 4 Activity 5 Activity 6 9
8 Course organization Class 1 Getting started with Weka Class 2 Evaluation Class 3 Simple classifiers Mid class assessment 1/3 Class 4 More classifiers Class 5 Putting it all together 10 Post class assessment 2/3
9 Textbook This textbook discusses data mining, and Weka, in depth: Data Mining: Practical machine learning tools and techniques, by Ian H. Witten, Eibe Frank and Mark A. Hall. Morgan Kaufmann, 2011 The publisher has made available parts relevant to this course in ebook format. 11
10 12 World Map by David Niblack, licensed under a Creative Commons Attribution 3.0 Unported License
11 Data Mining with Weka Class 1 Lesson 2 Exploring the Explorer Ian H. Witten Department of Computer Science University of Waikato New Zealand weka.waikato.ac.nz
12 Lesson 1.2: Exploring the Explorer Class 1 Getting started with Weka Lesson 1.1 Introduction Class 2 Evaluation Class 3 Simple classifiers Class 4 More classifiers Lesson 1.2 Exploring the Explorer Lesson 1.3 Exploring datasets Lesson 1.4 Building a classifier Lesson 1.5 Using a filter Class 5 Putting it all together Lesson 1.6 Visualizing your data 14
13 Lesson 1.2: Exploring the Explorer Download from (for Windows, Mac, Linux) Weka (the latest stable version of Weka) (includes datasets for the course) (it s important to get the right version, ) 15
14 Lesson 1.2: Exploring the Explorer Performance comparisons Graphical interface Command line interface 16
15 Lesson 1.2: Exploring the Explorer 17
16 Lesson 1.2: Exploring the Explorer attributes instances Outlook Temp Humidity Windy Play Sunny Hot High False No Sunny Hot High True No Overcast Hot High False Yes Rainy Mild High False Yes Rainy Cool Normal False Yes Rainy Cool Normal True No Overcast Cool Normal True Yes Sunny Mild High False No Sunny Cool Normal False Yes Rainy Mild Normal False Yes Sunny Mild Normal True Yes Overcast Mild High True Yes Overcast Hot Normal False Yes Rainy Mild High True No 18
17 Lesson 1.2: Exploring the Explorer open file weather.nominal.arff 19
18 Lesson 1.2: Exploring the Explorer attributes attribute values 20
19 Lesson 1.2: Exploring the Explorer Install Weka Get datasets Open Explorer Open a dataset (weather.nominal.arff) Look at attributes and their values Edit the dataset Save it? Course text Section 1.2 The weather problem Chapter 10 Introduction to Weka 21
20 Data Mining with Weka Class 1 Lesson 3 Exploring datasets Ian H. Witten Department of Computer Science University of Waikato New Zealand weka.waikato.ac.nz
21 Lesson 1.3: Exploring datasets Class 1 Getting started with Weka Lesson 1.1 Introduction Class 2 Evaluation Class 3 Simple classifiers Class 4 More classifiers Lesson 1.2 Exploring the Explorer Lesson 1.3 Exploring datasets Lesson 1.4 Building a classifier Lesson 1.5 Using a filter Class 5 Putting it all together Lesson 1.6 Visualizing your data
22 Lesson 1.3: Exploring datasets attributes instances Outlook Temp Humidity Windy Play Sunny Hot High False No Sunny Hot High True No Overcast Hot High False Yes Rainy Mild High False Yes Rainy Cool Normal False Yes Rainy Cool Normal True No Overcast Cool Normal True Yes Sunny Mild High False No Sunny Cool Normal False Yes Rainy Mild Normal False Yes Sunny Mild Normal True Yes Overcast Mild High True Yes Overcast Hot Normal False Yes Rainy Mild High True No 24
23 Lesson 1.3: Exploring datasets open file weather.nominal.arff attributes attribute values class 25
24 Lesson 1.3: Exploring datasets Classification sometimes called supervised learning Dataset: classified examples Model that classifies new examples classified example attribute 1 attribute 2 attribute n class instance: fixed set of features discrete: classification problem continuous: regression problem 26 discrete ( nominal ) continuous ( numeric )
25 Lesson 1.3: Exploring datasets open file weather.numeric.arff attributes attribute values class 27
26 Lesson 1.3: Exploring datasets open file glass.arff 28
27 Lesson 1.3: Exploring datasets The classification problem weather.nominal, weather.numeric Nominal vs numeric attributes ARFF file format glass.arff dataset Sanity checking attributes 29 Course text Section 11.1 Preparing the data Loading the data into the Explorer
28 Data Mining with Weka Class 1 Lesson 4 Building a classifier Ian H. Witten Department of Computer Science University of Waikato New Zealand weka.waikato.ac.nz
29 Lesson 1.4: Building a classifier Class 1 Getting started with Weka Lesson 1.1 Introduction Class 2 Evaluation Class 3 Simple classifiers Class 4 More classifiers Lesson 1.2 Exploring the Explorer Lesson 1.3 Exploring datasets Lesson 1.4 Building a classifier Lesson 1.5 Using a filter Class 5 Putting it all together Lesson 1.6 Visualizing your data 31
30 Lesson 1.4: Building a classifier Use J48 to analyze the glass dataset Open file glass.arff (or leave it open from the last lesson) Check the available classifiers Choose the J48 decision tree learner (trees>j48) Run it Examine the output Look at the correctly classified instances and the confusion matrix 32
31 Lesson 1.4: Building a classifier Investigate J48 Open the configuration panel Check the More information Examine the options Use an unpruned tree Look at leaf sizes Set minnumobj to 15 to avoid small leaves Visualize tree using right click menu 33
32 Lesson 1.4: Building a classifier From C4.5 to J48 ID3 (1979) C4.5 (1993) C4.8 (1996?) C5.0 (commercial) J48 34
33 Lesson 1.4: Building a classifier Classifiers in Weka Classifying the glass dataset Interpreting J48 output J48 configuration panel option: pruned vs unpruned trees option: avoid small leaves J48 ~ C Course text Section 11.1 Building a decision tree Examining the output
34 Data Mining with Weka Class 1 Lesson 5 Using a filter Ian H. Witten Department of Computer Science University of Waikato New Zealand weka.waikato.ac.nz
35 Lesson 1.5: Using a filter Class 1 Getting started with Weka Lesson 1.1 Introduction Class 2 Evaluation Class 3 Simple classifiers Class 4 More classifiers Lesson 1.2 Exploring the Explorer Lesson 1.3 Exploring datasets Lesson 1.4 Building a classifier Lesson 1.5 Using a filter Class 5 Putting it all together Lesson 1.6 Visualizing your data 37
36 Lesson 1.5: Using a filter Use a filter to remove an attribute Open weather.nominal.arff (again!) Check the filters supervised vs unsupervised attribute vs instance Choose the unsupervised attribute filter Remove Check the More information; look at the options Set attributeindices to 3 and click OK Apply the filter Recall that you can Save the result Press Undo 38
37 Lesson 1.5: Using a filter Remove instances where humidity is high Supervised or unsupervised? Attribute or instance? Look at them Select RemoveWithValues Set attributeindex Set nominalindices Apply Undo 39
38 Lesson 1.5: Using a filter Fewer attributes, better classification! Open glass.arff Run J48 (trees>j48) Remove Fe Remove all attributes except RI and MG Look at the decision trees Use right click menu to visualize decision trees 40
39 Lesson 1.5: Using a filter Filters in Weka Supervised vs unsupervised, attribute vs instance To find the right one, you need to look! Filters can be very powerful Judiciously removing attributes can improve performance increase comprehensibility Course text Section 11.2 Loading and filtering files 41
40 Data Mining with Weka Class 1 Lesson 6 Visualizing your data Ian H. Witten Department of Computer Science University of Waikato New Zealand weka.waikato.ac.nz
41 Lesson 1.6: Visualizing your data Class 1 Getting started with Weka Lesson 1.1 Introduction Class 2 Evaluation Class 3 Simple classifiers Class 4 More classifiers Lesson 1.2 Exploring the Explorer Lesson 1.3 Exploring datasets Lesson 1.4 Building a classifier Lesson 1.5 Using a filter Class 5 Putting it all together Lesson 1.6 Visualizing your data 43
42 Lesson 1.6: Visualizing your data Using the Visualize panel Open iris.arff Bring up Visualize panel Click one of the plots; examine some instances Set x axis to petalwidth and y axis to petallength Click on Class colour to change the colour Bars on the right change correspond to attributes: click for x axis; right click for y axis Jitter slider Show Select Instance: Rectangle option Submit, Reset, Clear and Save 44
43 Lesson 1.6: Visualizing your data Visualizing classification errors Run J48 (trees>j48) Visualize classifier errors (from Results list) Plot predictedclass against class Identify errors shown by confusion matrix 45
44 Lesson 1.6: Visualizing your data Get down and dirty with your data Visualize it Clean it up by deleting outliers Look at classification errors (there s a filter that allows you to add classifications as a new attribute) Course text Section 11.2 Visualization 46
45 Data Mining with Weka Department of Computer Science University of Waikato New Zealand Creative Commons Attribution 3.0 Unported License creativecommons.org/licenses/by/3.0/ weka.waikato.ac.nz
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