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classification - Chapter 3 Supervised Learning Most slides...

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Chapter 3: Supervised Learning Most slides courtesy Bing Liu
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2 Spring 2008 Web Mining Seminar Road Map Basic concepts Decision tree induction Evaluation of classifiers Rule induction Classification using association rules Naïve Bayesian classification Naïve Bayes for text classification Support vector machines K-nearest neighbor Ensemble methods: Bagging and Boosting Summary
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3 Spring 2008 Web Mining Seminar An example application A credit card company receives thousands of applications for new cards. Each application contains information about an applicant, age Marital status annual salary outstanding debts credit rating etc. Problem : to decide whether an application should approved, or to classify applications into two categories, approved and not approved .
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4 Spring 2008 Web Mining Seminar Machine learning and our focus Like humans learning from past experiences. A computer does not have “experiences”. A computer system learns from data, which represent some “past experiences” of an application domain. Our focus: learn a target function that can be used to predict the values of a discrete class attribute, e.g., approve or not-approved , and high-risk or low risk . The task is commonly called: supervised learning , classification , or inductive learning.
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5 Spring 2008 Web Mining Seminar Data: A set of data records (also called examples, instances or cases) described by k attributes : A 1 , A 2 , … A k . a class : Each example is labelled with a pre- defined class. Goal: To learn a classification model from the data that can be used to predict the classes of new (future, unseen test) cases/instances. The data and the goal
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6 Spring 2008 Web Mining Seminar An example: data (loan appl.) Approved or not
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7 Spring 2008 Web Mining Seminar An example: the learning task Learn a classification model from the data Use the model to classify future loan applications into Yes (approved) and No (not approved) What is the class for following case/instance?
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8 Spring 2008 Web Mining Seminar Supervised vs. unsup. learning Supervised learning: classification is seen as supervised learning from examples. Supervision : The data (observations, measurements, etc.) are labeled with pre-defined classes. It is as if a “teacher” gave the classes ( supervision ). Test data are classified into these classes too. Unsupervised learning (clustering) Class labels of the data are unknown Given a set of data, the task is to establish the existence of classes or clusters in the data
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9 Spring 2008 Web Mining Seminar Supervised learning: two steps Learning (training) : Build a model using the training data Testing: Test the model using unseen test data to assess the model accuracy , cases test of number Total tions classifica correct of Number = Accuracy
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10 Spring 2008 Web Mining Seminar What do we mean by learning?
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