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 […]
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 […]
The caret package (short for classi cation and regression training) contains functions to streamline the model training process for complex regression and classi cation problems. The package utilizes a number of R packages but tries not to load them all at package start-up1. The package “suggests” field includes 27 packages. caret loads packages as needed and assumes […]
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 […]
