ποΈ Week 5: Non-linear algorithms and ensemble methods
Theme: Supervised Learning
Welcome to the fifth week of this course!
This week, we wrap up our exploration of supervised learning techniques with some non-linear (classification) algorithms such as decision trees, random forests and support vector machines. Weβll also review how a procedure called cross-validation (which is a resampling technique) can be used to more robustly evaluate our models and to set their hyperparameters.
A homework formative about supervised learning techniques, in particular model evaluation and comparison as well as resampling (solutions released at the beginning of week 7) will be released this week. Itβs a good preparation for your first summative, also released this week and due on Week 08! Stay tuned.
π©π»βπ« Lecture Material
π₯ Looking for lecture recordings? You can only find those on Moodle, typically a day after the lecture. If you canβt find the recordings, please contact π§ .
Download the Quarto markdown file for this lecture:
Make sure to switch to the Python 12 environment from this week onwards (see this page for details)
You might be missing a few libraries for this lecture. You can install them by running
conda install -c conda-forge imbalanced-learn shap missingno