Registration is open for the course CLASSIFICATION AND REGRESSION TREES AND NEURAL NETWORKS WITH R - Second Edition.
INSTRUCTORS: Dr. Llorenç Badiella (UAB, Spain), Dr. Joan Valls (Biomedical Research Institute of Lleida, Spain) and Dr. Montserrat Martínez-Alonso (Biomedical Research Institute of Lleida, Spain).
DATES: November 4-7, 2013; 24 teaching hours.
PLACE: Premises of Sabadell of the Institut Català de Paleontologia Miquel Crusafont, Sabadell, Barcelona (Spain).
Organized by: Transmitting Science and the Institut Catalá de Paleontologia Miquel Crusafont.
More information: http://www.transmittingscience.org/cart_with_r.htm or writing to [log in to unmask]
The main goal of the methods such as CART (Classification and Regression Trees), is to model and predict one response variable explained by a set of dependent variables. This methods can be particularly effective to model interactions between explanatory variables. On the other hand, as a statistical model, a neural network is based on linear and non-linear combinations of explanatory variables that interact with other combinations to predict or explain an outcome variable. Both CART and neural networks methods can provide good results to explain or predict an outcome variable, particularly when the number of interactions is important. Nevertheless, these techniques also tend to over-fit the data and a validation of the models is required. ROC methods, including a sensitivity/specificity analyses and/or external validations can be performed to assess the consistency of these techniques. Applications cover a wide range of problems, including species classification in biology, prediction of the prognosis of a patient in biomedicine, etc.