lect12

lect12 - Lecture outline Classification Decision-tree...

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Lecture outline Classification Decision-tree classification
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What is classification?
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What is classification? Classification is the task of learning a target function f that maps attribute set x to one of the predefined class labels y
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What is classification?
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Why classification? The target function f is known as a classification model Descriptive modeling: Explanatory tool to distinguish between objects of different classes (e.g., description of who can pay back his loan) Predictive modeling: Predict a class of a previously unseen record
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credit approval target marketing medical diagnosis treatment effectiveness analysis Typical applications
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General approach to classification Training set consists of records with known class labels Training set is used to build a classification model The classification model is applied to the test set that consists of records with unknown labels
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General approach to classification
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Evaluation of classification models Counts of test records that are correctly (or incorrectly) predicted by the classification model Confusion matrix Class = 1 Class = 0 Class = 1 f 11 f 10 Class = 0 f 01 f 00 Predicted Class Actual Class 00 01 10 11 00 11 s prediction of # total s prediction correct # Accuracy f f f f f f + + + + = = 00 01 10 11 01 10 s prediction of # total s prediction wrong # rate Error f f f f f f + + + + = =
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Supervised vs. Unsupervised Learning Supervised learning (classification) Supervision: The training data (observations, measurements, etc.) are accompanied by labels indicating the class of the observations New data is classified based on the training set Unsupervised learning (clustering) The class labels of training data is unknown Given a set of measurements, observations, etc. with the aim of establishing the existence of classes or clusters in the data
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Decision Trees Decision tree A flow-chart-like tree structure Internal node denotes a test on an attribute Branch represents an outcome of the test Leaf nodes represent class labels or class distribution Decision tree generation consists of two phases Tree construction At start, all the training examples are at the root Partition examples recursively based on selected attributes Tree pruning Identify and remove branches that reflect noise or outliers Use of decision tree: Classifying an unknown sample Test the attribute values of the sample against the decision tree
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Training Dataset age income student credit_rating buys_computer <=30 high no fair no <=30 high no excellent 31…40 high
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lect12 - Lecture outline Classification Decision-tree...

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