dm3part1 - University of Florida CISE department...

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University of Florida CISE department Gator Engineering Classification Part 1 Dr. Sanjay Ranka Professor Computer and Information Science and Engineering University of Florida, Gainesville
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University of Florida CISE department Gator Engineering Data Mining Sanjay Ranka Spring 2011 Overview • Introduction to classification • Different techniques for classification • Decision Tree Classifiers – How decision tree works? – How to build a decision tree? – Methods for splitting – Measures for selecting the best split – Practical Challenges in Classification – Handling over-fitting – Handling missing attribute values – Other issues
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University of Florida CISE department Gator Engineering Data Mining Sanjay Ranka Spring 2011 Classification : Definition • Given a set of records (called the training set ) – Each record contains a set of attributes – One of the attributes is the class • Find a model for the class attribute as a function of the values of other attributes Goal : Previously unseen records should be assigned to a class as accurately as possible – Usually, the given data set is divided into training and test set, with training set used to build the model and test set used to validate it. The accuracy of the model is determined on the test set.
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University of Florida CISE department Gator Engineering Data Mining Sanjay Ranka Spring 2011 • In general a classification model can be used for the following purposes: – It can serve as a explanatory tool for distinguishing objects of different classes. This is the descriptive element of the classification model – It can be used to predict the class labels of new records. This is the predictive element of the classification model Classification Model
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University of Florida CISE department Gator Engineering Data Mining Sanjay Ranka Spring 2011 General Approach • To build a classification model, the labeled data set is initially partitioned in to two disjoint sets, known as training set and test set , respectively • Next, a classification technique is applied to the training set to induce a classification model • Each classification technique applies a learning algorithm
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University of Florida CISE department Gator Engineering Data Mining Sanjay Ranka Spring 2011 General Approach • The goal of a learning algorithm is to build a model that has good generalization capability – That is it must not only fit the training set well but can also predict correctly the class labels of many previously unseen records • To evaluate how well the induced model performs on records it has not seen earlier, we can apply it to the test set
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University of Florida CISE department Gator Engineering Data Mining Sanjay Ranka Spring 2011 General Approach Test Set Training Set Model Learn Classifier
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CISE department Gator Engineering Data Mining Sanjay Ranka Spring 2011 Classification Techniques • Decision Tree based Methods • Rule-based Methods • Memory based reasoning
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This note was uploaded on 11/13/2011 for the course CIS 4930 taught by Professor Staff during the Spring '08 term at University of Florida.

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dm3part1 - University of Florida CISE department...

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