Masters Degrees (Statistics and Actuarial Science)

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Browsing Masters Degrees (Statistics and Actuarial Science) by Subject "Artificial intelligence -- South Africa"

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    Interpretation of an artificial neural network as a black box model
    (Stellenbosch : Stellenbosch University, 2022-04) Laubscher, Karla; Uys, Danie W.; Stellenbosch University. Faculty of Economic and Management Sciences. Dept. of Statistics and Actuarial Science.
    ENGLISH SUMMARY: In the field of machine learning, the main objective is generally to use machine learning models to study patterns and extract information from available data. The knowledge gained by the model can be used for inference and making possible future predictions for unseen data. Artificial neural networks (ANNs) are one of the most powerful models for solving machine learning problems that are of a complex nature. Complex machine learning problems may include non-linear problems, and, in this case, a simple linear model is not an appropriate solution. ANNs have been proven to have high prediction accuracy as a result of learning from its training process. ANNs have been grouped in the class of machine learning models that are viewed as a black box. Black box models are defined by a lack of transparency and interpretability. These types of models cannot be easily explained or understood by humans and therefore create a lack of trust in the model in its entirety. Several methods have been developed to improve the interpretability of ANNs. These methods can be categorised into two groups: model-specific methods that are unique to a certain class of machine learning models, and model-agnostic methods that can be used for interpretation of various machine learning models irrespective of the model type. Model-specific methods used in this study include a neural interpretation diagram (NID), Garson’s algorithm, and the partial derivatives method. These three methods focus on the structure of the ANN and more specifically try to compute variable importance by investigating the connection weights in the network. Model-agnostic methods used in the study include partial dependence plots (PDP), permutation feature importance, and the global surrogate method. An overview of ANNs and methods for model interpretation are discussed in a literature review. This is followed by a research methodology with a more detailed discussion on the relevant methods. A simulation study is done to investigate and compare selected methods for model interpretation. The selected methods are Garson’s algorithm, permutation feature importance, and the global surrogate method. Finally, a practical data set is investigated using an ANN for making predictions. The methods for model interpretation discussed in the study are implemented in the practical study with the goal of identifying features that are significant to the ANN model.