Foundations of Machine LearningEnsemble Learning （集成学习）Top 10 algorithms in data miningC4.5K-MeansSVMAprioriEM (Maximum Likelihood)PageRankAdaBoostKNNNaïveBayesCARTEnsemble LearningIntroductionCommonly used ensemble learning algorithmsBaggingRandom forestBoostingsklearn.ensemble: Ensemble MethodsEnsemble LearningLesson 7 - 3Introduction Someone wants to invest in a company XYZ. He is not sure about its performance though. So, he looks for advice on whether the stock price will increase more than 6% per annum or not? He decides to approach various experts having diverse domain experience: Employee of Company XYZ: right 70% times. Financial Advisor of Company XYZ: right 75% times. Stock Market Trader : right 70% times. Employee of a competitor : right 60% times. Market Research team in same segment : right 75% times. Social Media Expert : right 65% times.Ensemble LearningLesson 7 - 4Introduction Someone wants to invest in a company XYZ. He is not sure about its performance though. So, he looks for advice on whether the stock price will increase more than 6% per annum or not? He decides to approach various experts having diverse domain experience: In a scenario when all the 6 experts/teams verify that it’s a good decision (assuming all the predictions are independent of each other), we will get a combined accuracy rate of: 1-30%*25%*30%*40%*25%*35%=99.92125%Ensemble LearningLesson 7 - 5Definition Ensemble learning is a machine learning paradigm where multiple learners are trained to solve the same problem. Also, called multi-classifier system （多分类器系统） , or committee-based learning （基于委员会的学习） . In contrast to ordinary machine learning approaches which try to lear...
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