Machine Learning in Epidemiology with R: From Trees to Neural Networks
How do different machine-learning models learn from the same data, and how can we compare their predictions?
Join R-Ladies Rome for the second workshop in our Machine Learning in Epidemiology with R series, led by Federica Gazzelloni.
Part I introduced how models learn through loss and optimisation.
Part II, we’ll put these ideas into practice by training and comparing decision trees, random forests and a simple neural network using a heart-disease classification example.
We’ll follow a complete workflow in R: explore the data, define a prediction task, train models and evaluate their performance on observations they have not seen during training.
What we’ll cover
Decision trees and random forests with
mlr3.A simple neural network with Keras.
Layers, activation functions, epochs and batch sizes.
Training and validation loss.
Model comparison using accuracy, sensitivity, specificity and ROC/AUC.
Overfitting, cross-validation and the distinction between prediction and causation.
Who is this for?
This workshop is for people interested in applying machine learning to epidemiology and health data. Basic familiarity with R and RStudio is recommended. The session builds on Part I and begins with a short recap.
What to bring
A computer with R and RStudio installed. Setup instructions and workshop materials will be shared before the session.
Bring your questions and join us for a practical exploration of how different models learn—and how we assess their predictions.