Predictive Analytics with R
This class will provide hands-on instruction for using machine learning algorithms to predict a disease outcome. We will cover data cleaning, feature extraction, imputation, and using a variety of models to try to predict disease outcome. We will use resampling strategies to assess the performance of predictive modeling procedures such as Random Forest, stochastic gradient boosting, elastic net regularized regression (LASSO), and k-nearest neighbors. We will also demonstrate demonstrate how to forecast future trends given historical infectious disease surveillance data using methodology that accounts for seasonality and nonlinearity.
Prerequisites: This is not a beginner R class. Familiarity with R, installing/using packages, importing/saving results, expertise with data manipulation using dplyr and visualization with ggplot2 are all required
- Wednesday, March 6, 2019
- 9:00am - 12:00pm
- Health Sciences Library Carter Classroom
- David Martin