Evolutionary algorithms in statistical learning : Automating the optimization procedure
Sammanfattning: Scania has been working with statistics for a long time but has invested in becoming a data driven company more recently and uses data science in almost all business functions. The algorithms developed by the data scientists need to be optimized to be fully utilized and traditionally this is a manual and time consuming process. What this thesis investigates is if and how well evolutionary algorithms can be used to automate the optimization process. The evaluation was done by implementing and analyzing four variations of genetic algorithms with different levels of complexity and tuning parameters. The algorithm subject to optimization was XGBoost, a gradient boosted tree model, applied to data that had previously been modelled in a competition. The results show that evolutionary algorithms are applicable in finding good models but also emphasizes the importance of proper data preparation.
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