An Exercise in Machine Learning

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Presentation transcript:

An Exercise in Machine Learning http://www.cs.iastate.edu/~cs573x/BBSIlab/2006/ Cornelia Caragea

Outline Machine Learning Software Preparing Data Building Classifiers Interpreting Results

Machine Learning Software Suites (General Purpose) WEKA (Source: Java) MLC++ (Source: C++) SAS List from KDNuggets (Various) Specific Classification: C4.5, SVMlight Association Rule Mining Bayesian Net … Commercial vs. Free

What does WEKA do? 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…)

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…

Outline Machine Learning Software Preparing Data Building Classifiers Interpreting Results

Preparing Data ARFF Data Format Header – describing the attribute types Data – (instances, examples) comma-separated list

Launching WEKA java -jar weka.jar

Load Dataset into WEKA

Data Filters 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

Data Filters

Outline Machine Learning Software Preparing Data Building Classifiers Interpreting Results

Building Classifiers 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!

Building Classifiers

(1) weka.classifiers.rules.ZeroR 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)

Exercise 1 http://www.cs.iastate.edu/~cs573x/BBSIlab/2006/exercises/ex1.html

(2)weka.classifiers.bayes.NaiveBayes Class for building a Naive Bayes classifier

(3) weka.classifiers.trees.J48 Class for generating a pruned or unpruned C4.5 decision tree

Test Options Percentage Split (2/3 Training; 1/3 Testing) Cross-validation estimating the generalization error based on resampling when limited data; averaged error estimate. stratified 10-fold leave-one-out (Loo)

Outline Machine Learning Software Preparing Data Building Classifiers Interpreting Results

Understanding Output

Decision Tree Output (1)

Decision Tree Output (2)

Exercise 2 http://www.cs.iastate.edu/~cs573x/BBSIlab/2006/exercises/ex2.html

Performance Measures Accuracy & Error rate Confusion matrix – contingency table True Positive rate & False Positive rate (Area under Receiver Operating Characteristic) Precision,Recall & F-Measure Sensitivity & Specificity For more information on these, see uisp09-Evaluation.ppt

Decision Tree Pruning Overcome Over-fitting Pre-pruning and Post-pruning Reduced error pruning Subtree raising with different confidence Comparing tree size and accuracy

Subtree replacement Bottom-up: tree is considered for replacement once all its subtrees have been considered

Subtree Raising Deletes node and redistributes instances Slower than subtree replacement

Exercise 3 http://www.cs.iastate.edu/~cs573x/BBSIlab/2006/exercises/ex3.html