大数据数据挖掘培训讲义4：知识呈现.pptVIP

COM 307: Machine Learning and Data Mining Knowledge Representation Output: representing structural patterns Many different ways of representing patterns Decision trees, rules, instance-based, … Also called “knowledge” representation Representation determines inference method Understanding the output is the key to understanding the underlying learning methods Different types of output for different learning problems (e.g. classification, regression, …) Decision tables Simplest way of representing output: Use the same format as input! Decision table for the weather problem: What do you think is the main problem? Decision tables Decision table for the weather problem: Main problem: selecting the right attributes Also, not flexible enough Decision trees, 1 “Divide-and-conquer” approach produces tree Nodes involve testing a particular attribute Usually, attribute value is compared to constant Other possibilities: Comparing values of two attributes Using a function of one or more attributes Leaves assign classification, set of classifications, or probability distribution to instances Unknown instance is routed down the tree Decision trees, 2 Build a decision tree using this information: Decision trees, 3 Nominal and numeric attributes Nominal:number of children usually equal to number values? attribute won’t get tested more than once Other possibility: division into two subsets Numeric:test whether value is greater or less than constant? attribute may get tested several times Other possibility: three-way split (or multi-way split) Integer: less than, equal to, greater than Real: below, within, above Missing values Does absence of value have some significance? Yes ? “missing” is a separate value No ? “missing” must be treated in a special way Solution A: assign instance to most popular branch Solution B: split instance into pieces Pieces receive weight according to fraction of training instances that go down each branch Classifications from leave nodes ar