The Shapley value method is an explanatory method that describes the feature attribution of Machine Learning models. There are three different definitions of the Shapley values, namely Conditional Expectation Shapley, Marginal Expectation Shapley and Baseline Shapley. A comparison is made between the three definitions and they are applied to one statistical and two Machine Learning models that predict house transaction prices. Most existing methods to approximate Shapley values assume independence, which is in practice often violated. An existing copula-based method that tries to take into account the dependency is extended to apply to problems with continuous and discrete features. This copula-based method approximates the Shapley values more accurately than other methods. The Conditional Expectation Shapley values give unnatural explanations, therefore other definitions of the Shapley values are more suitable. The Baseline Shapley values seem to be the most promising since there is an accurate and fast approximation method and the B Shapley values are the easiest to interpret.

Collective dynamics is something that can be found in nature on macro and micro scale. Since the 90s of the previous century researchers have been interested in finding a model for this group behaviour. The dynamics of a group as a whole is only determined by shortrange interactions of the individuals. To better understand the working of this process, we make a model of this system with soft two-dimensional spheres with a active selfpropulsion force. Furthermore there are repulsion, alignment and noise interactions, all depending exclusively on nearest neighbours. We focus on binary systems with particles of two different sizes. Migrating and rotating states are typically found in systems with homogeneous sizes. We show that these states are also found in binary systems. The migrating state has circulation of particles and this leads to segregation of the small and big particles. Small particles are more likely to be found in the tip of the group, while big particles accumulate at the tail. Active noise in the system plays a role in the degree of segregation. The lower the noise is, the more segregated the system gets in the end.