1. Machine Learning with Weka
Cornelia Caragea
Thanks to Eibe Frank for some of the slides

2. Outline Weka: A Machine Learning Toolkit Preparing Data
Building Classifiers
Implementation of the state-of-the-art learning algorithm
Main strengths in the classification
Regression, Association Rules and clustering algorithms
Extensible to try new learning schemes
Large variety of handy tools (transforming datasets, filters, visualization etc…)

3. WEKA: the software
Machine learning/data mining software written in Java (distributed under the GNU Public License)
Used for research, education, and applications
Main features:
Comprehensive set of data pre-processing tools, learning algorithms and evaluation methods
Graphical user interfaces (incl. data visualization)
Environment for comparing learning algorithms
Collection of machine learning algorithms for data mining tasks
Applied directly to a dataset
Called from your own Java code

4. WEKA: versions There are several versions of WEKA:
WEKA: “book version” compatible with description in data mining book
WEKA: “GUI version” adds graphical user interfaces (book version is command-line only)
WEKA: “development version” with lots of improvements

5. WEKA: resources API Documentation, Tutorials, Source code.
WEKA mailing list
Data Mining: Practical Machine Learning Tools and Techniques with Java Implementations
Weka-related Projects:
Weka-Parallel - parallel processing for Weka
RWeka - linking R and Weka
YALE - Yet Another Learning Environment
Many others…

6. Weka: web site

7. WEKA: launching
java -jar weka.jar

8. Outline Weka: A Machine Learning Toolkit Preparing Data
Building Classifiers

9. WEKA only deals with “flat” files
@relation heart-disease-simplified
@attribute age numeric
@attribute sex { female, male}
@attribute chest_pain_type { typ_angina, asympt, non_anginal, atyp_angina}
@attribute cholesterol numeric
@attribute exercise_induced_angina { no, yes}
@attribute class { present, not_present}
@data
63,male,typ_angina,233,no,not_present
67,male,asympt,286,yes,present
67,male,asympt,229,yes,present
38,female,non_anginal,?,no,not_present
...
ARFF Data Format
Header – describing the attribute types
Data – (instances, examples) comma-separated list
Flat file in
ARFF format

10. WEKA only deals with “flat” files
numeric attribute
@relation heart-disease-simplified
@attribute age numeric
@attribute sex { female, male}
@attribute chest_pain_type { typ_angina, asympt, non_anginal, atyp_angina}
@attribute cholesterol numeric
@attribute exercise_induced_angina { no, yes}
@attribute class { present, not_present}
@data
63,male,typ_angina,233,no,not_present
67,male,asympt,286,yes,present
67,male,asympt,229,yes,present
38,female,non_anginal,?,no,not_present
...
nominal attribute

11. Explorer: pre-processing the data
Data can be imported from a file in various formats: ARFF, CSV, binary
Data can also be read from a URL or from an SQL database (using JDBC)
Pre-processing tools in WEKA are called “filters”
WEKA contains filters for:
Discretization, normalization, resampling, attribute selection, …
Useful support for data preprocessing
Removing or adding attributes, resampling the dataset, removing examples, etc.
Creates stratified cross-validation folds of the given dataset, and class distributions are approximately retained within each fold.
Typically split data as 2/3 in training and 1/3 in testing

22. Outline Weka: A Machine Learning Toolkit Preparing Data
Building Classifiers

23. Explorer: building “classifiers”
Classifiers in WEKA are models for predicting nominal or numeric quantities
Implemented learning schemes include:
Decision trees, support vector machines, perceptrons, neural networks, logistic regression, Bayes nets, …
“Meta”-classifiers include:
Bagging, boosting, stacking, …
A classifier model - mapping from dataset attributes to the class (target) attribute. Creation and form differs.
Decision Tree and Naïve Bayes Classifiers
Which one is the best?
No Free Lunch!

27. Class for building and using a 0-R classifier
Majority class classifier
Predicts the mean (for a numeric class) or the mode (for a nominal class)

32. Outline
Machine Learning Software
Preparing Data
Building Classifiers

33. To Do
Try Naïve Bayes and Logistic Regression classifiers on a different Weka dataset
Use various parameters
Try Linear regression