In this Python notebook, based on a large French. The results are compared and the interpretability of the models is analyzed and evaluated with SHAP and PDP plots. In addition, the four tools TPOT, Auto-Sklearn, H2O and FLAML are tested or used. In this Python notebook, loss frequency models are created on the basis of a large French motor liability portfolio using a GLM as well as machine learning methods (see also R-notebook ADS use case 1 of DAV), the results are compared and at the end the interpretability of the models is analyzed and evaluated using SHAP and PDP plots. For this year's focus on automated machine learning (Auto-ML), the four tools TPOT, Auto-Sklearn, H2O and FLAML are additionally tested or used. The difficulties currently encountered when using Auto-ML tools to implement common actuarial methods such as Poisson case number modeling become apparent. The analysis is very accessible, relevant, and novel in this compilation.
The German Association of Actuaries (Deutsche Aktuarvereinigung e.V., DAV) is the professional representation of all actuaries in Germany. It was founded in 1993 and has more than 5,400 members today. More than 700 members are involved in thirteen committees and in over 60 working groups as a voluntary commitment.
The given repositories have been created by committees and working groups and serve as an aid for our members and interested persons to support them in their work with machine learning methods and data science issues in an actuarial context.
Please note that the repositories provided on GitHub are published by the DAV. The content of linked websites is the sole responsibility of their operators. The DAV is not responsible for the code and data linked to Kaggle.com and referred to in the repositories. These reflect the individual opinion of each user on Kaggle.