Machine Learning predicting quality of exercise performed using smart wearable device data.

Background Using devices such as Jawbone Up, Nike FuelBand, and Fitbit it is now possible to collect a large amount of data about personal activity relatively inexpensively. These type of devices are part of the quantified self movement – a group of enthusiasts who take measurements about themselves regularly to improve their health, to find […]

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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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