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web数据挖掘课件CS583-supervised-learningPPT
Chapter 3: Supervised Learning;Road Map;An example application;Another application;Machine learning and our focus;An example: the learning task;Supervised vs. unsupervised Learning;Supervised learning process: two steps;What do we mean by learning?;An example;Fundamental assumption of learning;Road Map;Introduction;The loan data (reproduced);A decision tree from the loan data;Use the decision tree;Is the decision tree unique?;From a decision tree to a set of rules;Algorithm for decision tree learning;Decision tree learning algorithm;Choose an attribute to partition data ;The loan data (reproduced);Two possible roots, which is better?;Information theory;Information theory (cont …);Information theory: Entropy measure;Entropy measure: let us get a feeling;Information gain;Information gain (cont …);An example;We build the final tree;Handling continuous attributes;An example in a continuous space;Avoid overfitting in classification;An example;Other issues in decision tree learning;Road Map;Evaluating classification methods;Evaluation methods;Evaluation methods (cont…);Evaluation methods (cont…);Evaluation methods (cont…);Classification measures;Precision and recall measures;Precision and recall measures (cont…);An example;F1-value (also called F1-score);Another evaluation method: Scoring and ranking;Ranking and lift analysis;An example;An example;Lift curve;Road Map;Introduction;Sequential covering;Algorithm 1: ordered rules;Algorithm 2: ordered classes;Algorithm 1 vs. Algorithm 2;Learn-one-rule-1 function;Learn-one-rule-1 function (cont …);Learn-one-rule-1 algorithm;Learn-one-rule-2 function;Learn-one-rule-2 algorithm;Rule evaluation in learn-one-rule-2;Rule pruning in learn-one-rule-2;Discussions;Road Map;Three approaches;Using Class Association Rules;Decision tree vs. CARs;There are many more rules;Decision tree vs. CARs (cont …);Considerations in CAR mining;Building classifiers;CBA: Rules are sorted first;Classifier building using CARs;Using rules as features;Usi
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