Explainable modeling in machine learning : A comparative study

Sammanfattning: As the use of advanced machine learning models has increased, the need for explainability that these models lack concerning their prediction has increased simultaneously. The aim of this thesis is to compare different functions available in the program R regarding their ability to provide explainability for these advanced machine learning models, also commonly referred to as black-box models. In this thesis we compare eight functions. Four well known black-box models are implemented on four different datasets in order to compare the functions’ ability to provide explanation in different settings. In our comparative analysis, we evaluate various aspects to assess and contrast each function, including explainability, flexibility, and functionality.  Regardless of which model or dataset the explainability functions are exposed to, they are all capable of producing explainability plots. A result that showcases the high level of flexibility that every function holds. The result also provides an insight into how there is not one optimal function suitable for all of the models and datasets. All of the functions instead possess various advantages and disadvantages depending on the complexity of the models and which type of data being used. It is also evident that the number of included features and level of independence between the features has various effects on different functions. In conclusion, the functions in this thesis displayed a combination of significant flexibility and explainability, providing straightforward approaches to addressing the challenge of explainability in black-box models.