Comparing accuracy of prediction of Alzheimer’s diagnosis by Machine Learning Algorithms Random Forest, Boosted Trees and Linear Discriminant Analysis to a stack prediction of all the three Algorithms.

In this post, what I want to show is how the Machine Learning algorithms Random Forest, Boosted Trees and Linear Discriminant Analysis will compare to a stack or an ensemble of all of them together. Load the Alzheimer’s data using the following commands library(caret) library(gbm) set.seed(3433) library(AppliedPredictiveModeling) data(AlzheimerDisease) adData = data.frame(diagnosis,predictors) inTrain = createDataPartition(adData$diagnosis, p […]

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Notes on SDLC Models

Waterfall The Waterfall Model assumes that distinct phases of development of a whole system exist for analysis, preliminary design, detailed design, coding and unit test, and system test. Documentation produced at the end of each phase provides the basis for work performed in the next phase of development. In the Waterfall Model, characterized by a […]

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