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Tutorial “Introduction to Machine Learning with R” (half-day, morning), Przemyslaw Biecek
– The two cultures – To predict or to understand
– Evaluation of black-box models (RMSE, AUC, ROC, F1, LIFT, train-test split, CV-folds)
– Towards complex models – bias variance tradeoff (random forest, bagging, boosting)
– Explanatory Model Analysis – global level (partial dependence profiles, feature importance)
– Explanatory Model Analysis – local level (Shapley values, ICE/CP)
– The multiplicity of good models – Rashomon effect
– From complex to interpretable – how to extract features from black-box models
With a hands-on part based on R examples with medical examples.
– Introduction to Statistical Learning: http://faculty.marshall.usc.edu/gareth-james/ISL/
– Explanatory Model Analysis: https://github.com/pbiecek/ema
– mlr3 book: https://mlr3book.mlr-org.com/
Przemyslaw Biecek graduated in Mathematical Statistics and Software Engineering from Wroclaw University of Technology. He obtained his PhD in biostatistics, after which he held positions at Hasselt University (Belgium), Davis University (USA) and Nanyang Technological University (Singapore). He is now an Associate Professor in Machine Learning at Warsaw University of Technology and University of Warsaw.
His main areas of research include model visualization, model interpretability and predictive modelling of large and complex data with a special focus on applications in personalized computational medicine/oncology. He is also interested in evidence-based medicine and statistical software engineering (an R enthusiast).