The objective of this lab is to introduce Trees, Tree pruning and then boosting by use of random forests. Both classification and estimation problems are studied.
1. First steps with classification trees : the importance of depth, impurity function and visualization of trees ['N1_Classif_tree.ipynb'](https://gricad-gitlab.univ-grenoble-alpes.fr/ai-courses/autonomous_systems_ml/-/blob/master/notebooks/9_Trees_Boosting/N1_Classif_tree.ipynb)
2. Some fundamentals on regression trees ['N2_a_Regression_tree.ipynb'](https://gricad-gitlab.univ-grenoble-alpes.fr/ai-courses/autonomous_systems_ml/-/blob/master/notebooks/9_Trees_Boosting/N2_a_Regression_tree.ipynb)
3. Adapting the complexity to the data : Cost complexity pruning on a regression example. ['N2_b_Cost_Complexity_Pruning_Regressor.ipynb'](https://gricad-gitlab.univ-grenoble-alpes.fr/ai-courses/autonomous_systems_ml/-/blob/master/notebooks/9_Trees_Boosting/N2_b_Cost_Complexity_Pruning_Regressor.ipynb)
4. Boosting : random forest regression case study ['N3_a_Random_Forest_Regression.ipynb'](https://gricad-gitlab.univ-grenoble-alpes.fr/ai-courses/autonomous_systems_ml/-/blob/master/notebooks/9_Trees_Boosting/N3_a_Random_Forest_Regression.ipynb)
5. Boosting : random forest classification case study ['N3_b_Random_Forest_Classif.ipynb'](https://gricad-gitlab.univ-grenoble-alpes.fr/ai-courses/autonomous_systems_ml/-/blob/master/notebooks/9_Trees_Boosting/N3_a_Random_Forest_Classif.ipynb)
