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Lecture 5_LDA1

# Lecture 5_LDA1 - Classification Supervised learning and...

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Unformatted text preview: Classification: Supervised learning, and Model Evaluation Introduction to Discriminant Analysis and Classification Tommy W. S. Chow What is classification Illustrating Classification Task Apply Model Induction Deduction Learn Model Model Tid Attrib1 Attrib2 Attrib3 Class 1 Yes Large 125K No 2 No Medium 100K No 3 No Small 70K No 4 Yes Medium 120K No 5 No Large 95K Yes 6 No Medium 60K No 7 Yes Large 220K No 8 No Small 85K Yes 9 No Medium 75K No 10 No Small 90K Yes 10 Tid Attrib1 Attrib2 Attrib3 Class 11 No Small 55K ? 12 Yes Medium 80K ? 13 Yes Large 110K ? 14 No Small 95K ? 15 No Large 67K ? 10 Test Set Learning algorithm Training Set i.e., Classify if a man is a business man or not Classification: Definition Given a collection of records ( training set )- Each record contains a set of attributes , one of the attributes is the class . Find a model for class attribute as a function of the values of other attributes. Goal: previously unseen records should be assigned a class as accurately as possible.- A test set is used to determine the accuracy of the model. Usually, the given data set is divided into training and test sets, with training set used to build the model and test set used to validate it. Examples of Classification Task Predicting tumor cells as benign or malignant Classifying credit card transactions as legitimate or fraudulent Classifying faulty or normal system operations. If it is a faulty system, what type of fault is it? Categorizing news stories, finance, politics, sport, entertainment, etc Classifying problematic students from good student Predicting US president election, McCain or Obama Brazil Black Out 10 Nov 2009 Fish classification: Sea bass or salmon Histograms for the lightness feature. It shows no single threshold value x* (decision boundary) will be prefect for separating the two classes completely Using two features together (2-dimensional case) Using the 2 features together. The dark line is the decision boundary. Overall classification is higher than using any of the 1 feature alone Too complex model for the fish leads to too complicated model. It has high classification rate in this data set but will surely lead to poor performance for future patterns An optimal decision boundary tradeoff between performance on the training set and future classification data Classification Techniques Generative model This approach is based on statistical and probabilistic modeling of the happenings: such as Linear Discriminate Analysis (LDA), and Naïve Bayes classifier Discriminative model Decision Tree based Methods Support Vector Machine (SVM) Neural Networks K Nearest Neighbor Here is a dataset 48,000 records, 16 attributes [Kohavi 1995] Predicting wealth from age Carnegie Mellon University (CMU) , AI notes Predicting wealth from age Persons whose age are 20-30 are the most possible poor; ones whose age are 50-60 are the most possible rich Carnegie Mellon University (CMU) , AI notes Wealth from hours worked Carnegie Mellon University (CMU) , AI notes Wealth from years of education...
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Lecture 5_LDA1 - Classification Supervised learning and...

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