Tools for automating the testing and evaluation of education early warning system models
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README.md

EWStools

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Overview

EWStools is the source-code behind many of the modules within the Wisconsin Dropout Early Warning System created by the Wisconsin Department of Public Instruction. While the framework was designed particularly to the development of early-warning predictive models on education data, these tools represent a more generalized framework for building, testing, and exploring models built through the train function in the R package caret. As such, this package extends the features of caret to make it more efficient to search across model types, explore model performance on test and training data, and to draw ROC comparisons of classification models specifically.

EWStools is currently in beta and many of the functions are changing regularly.

Features

EWStools provides two distinct sets of features for model builders. The first is tools to automate the search for the best fitting model across model types. The second set of features is the creation of a new object class, ROCit objects, which allow for the easy comparison of ROC performance of classification models on both test and training data.

Model Search

EWStools features wrapper code for caret's train function which makes it easy to build a sequential test of many model types available to train and store the results of the test efficiently.