{"title":"Decision Trees and Random Forests","authors":"Tom Rainforth","doi":"10.1002/9781119544678.ch10","DOIUrl":"https://doi.org/10.1002/9781119544678.ch10","url":null,"abstract":"Y = { 1 if X1 > 0.4 and X2 > 0.6 0 otherwise We construct the dataset: n <5000 x <cbind(runif(n), runif(n)) y <factor(ifelse(x[,1] > .4, x[,2] > .6, 0)) r <data.frame(x, y) We construct a decision tree for this using rpart: tree <rpart(y ~ X1 + X2, data = r, method = \"class\") printcp(tree) We can generate a simple diagram of this tree: plot(tree, compress = TRUE, mar = c(.2, .2, .2, .2)) text(tree, use.n = TRUE) We can generate predictions using this tree on data using the predict function. Here we generate a testing set the same way as the training set above and we find the accuracy of our classifier:","PeriodicalId":344200,"journal":{"name":"Condition Monitoring with Vibration Signals","volume":"55 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125713587","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Time Domain Analysis","authors":"H. Blinchikoff, A. Zverev","doi":"10.1049/SBEW008E_ch1","DOIUrl":"https://doi.org/10.1049/SBEW008E_ch1","url":null,"abstract":"In this chapter we have consolidated the mathematical developments necessary to relate the physical system and the differential equation describing it. This included the derivation of the impulse response, step response, and convolution integral. These quantities, which are important to the theory of filtering, were shown to be a function of the L.I. solutions of the homogeneous equation. It is hoped that this time-domain approach has clarified the transition from the basic differential equation concepts to the everyday tools of the linear system analyst.","PeriodicalId":344200,"journal":{"name":"Condition Monitoring with Vibration Signals","volume":"50 5","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133239298","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Machinery Vibration Data Resources and Analysis Algorithms","authors":"","doi":"10.1002/9781119544678.app1","DOIUrl":"https://doi.org/10.1002/9781119544678.app1","url":null,"abstract":"","PeriodicalId":344200,"journal":{"name":"Condition Monitoring with Vibration Signals","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129219599","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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