Jue Wang (Joyce) Department of Computer Science, University of Massachusetts, Boston Feb Outline

Transcription

1 Learn to Use Weka Jue Wang (Joyce) Department of Computer Science, University of Massachusetts, Boston Feb Outline Introduction of Weka Explorer Filter Classify Cluster Experimenter KnowledgeFlow Simple CLI 2

2 What is Weka? Copyright: Martin Kramer Waikato Environment for Knowledge Analysis 3 Introduction of Weka Machine learning/data mining software written in Java (distributed under the GNU Public License) Support MS Windows, Mac OS X and GNU/Linux Used for research, education, and applications Main features: Comprehensive set of data pre-processing processing tools, learning algorithms and evaluation methods Graphical user interfaces (incl. data visualization) Environment for comparing learning algorithms 4

3 Data Set % This is a toy example, the UCI weather dataset. Comment lines at the beginning of the dataset should give an % Any relation to real weather is purely coincidental. indication of its source, context and golfweathermichigan_1988/02/10_14days Here we state the internal name of the dataset. Try to be as comprehensive as outlook {sunny, overcast, windy {TRUE, temperature humidity real Here we define two nominal attributes, outlook and windy. The former has three values: sunny, overcast and rainy; the latter two: TRUE and FALSE. Nominal values with special characters, commas or spaces are enclosed in single quotes. These lines define two numeric attributes. Instead of real, integer or numeric can also be used. While double floating point values are stored internally, only seven decimal digits are usually play {yes, no} The last attribute is the df default target or class variable ibl used for prediction. In our case it is a nominal attribute with two values, making this a binary classification sunny,false,85,85,no sunny,true,80,90,no overcast,false,83,86,yes rainy,false,70,96,yes rainy,false,68,?,yes,,,y The rest of the dataset consists of the followed by comma separated values for the attributes t one line per example. In our case there are five examples. More details: 5 Explorer 6

4 Explorer Filter transforms datasets: removing or adding attributes resampling the dataset removing examples 7 Explorer Filter 8

5 Classify Explorer 9 Explorer Cluster 10

6 Experimenter 11 Experimenter Experimenter makes it easy to compare the performance of different learning schemes For classification and regression problems Results can be written into file or database Evaluation options: cross-validation, learning curve, hold-out Can also iterate over different parameter settings Significance-testing built in! 12

7 KnowledgeFlow 13 KnowledgeFlow New graphical user interface for WEKA Java-Beans-based interface for setting up and running machine learning experiments Data sources, classifiers, etc. are beans and can be connected graphically Data flows through components: e.g., data source -> filter -> classifier -> evaluator Layouts can be saved and loaded again later 14

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17 Simple CLI 33 Simple CLI The Simple CLI provides full access to all Weka classes, i.e., classifiers, filters, clusterers, etc., but without the hassle of the CLASSPATH (it facilitates the one, with which Weka was started). The Simple CLI is the place for you test the code calling Weka from other program. 34

18 Simple CLI java <classname> [<args>] invokes a java class with the given arguments (if any) break stops the current thread, e.g., a running classifier, in a friendly manner kill cls exit help [<command>] stops the current thread, e.g., a running classifier, in a friendly manner clears the output area exits the Simple CLI provides an overview of the available commands if without a command name as argument, otherwise more help on the specified command 35 Thanks 36