18C5T13 Study Guide - Final Guide: Loss Function, Jaccard Index, Univariate
11 Aug 2021
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Unit 1 unit -i:the ingredients of machine learning, tasks: the problems that can be solved with machine learning, models: the output of machine learning, Binary classification and related tasks: classification, scoring and ranking, class probability estimation. Unit 2 unit- ii:beyond binary classification:handling more than two classes, Concept learning: the hypothesis space, paths through the hypothesis space, beyond conjunctive concepts. Find least general conjunctive generalization of two conjunctions, employing internal disjunction. Write in detailed note on multi class probabilities from coverage counts. How to handle more than two classes in beyond binary classification. Explain the following: one-versus-one voting, loss based decoding, coverage counts as scores. Unit 3 unit- iii: tree models: decision trees, ranking and probability estimation trees, tree learning as variance reduction. Rule models:learning ordered rule lists, learning unordered rule sets, descriptive rule learning, first-order rule learning. How do you define best split(d,f) for classification. Explain rule set for ranking and probability estimation.
