An Actuary learns Machine Learning – Part 3 – Automatic testing/feature importance/K-fold cross validation
In which we don’t actually improve our model but we do improve our workflow - being able to check our test score ourselves, analysing the importance of each variable using an algorithm, and then using an algorithm to select the best hyper-parameters
In which we build our first machine learning model in Python, beat our previous Excel model on our first attempt, and then fail multiple time to improve this new model…
In which we enter a machine learning competition, predict who survived the titanic, build an Excel model, and then realise it performs no better than Kaggle’s ‘test submission’...
I work as a pricing actuary at a reinsurer in London.