Machine learning models are used in many domains where wrong decisions can have severe consequences for the individual and society. Misclassifications and their causes are often difficult to detect, especially when using complex models whose decision-making behaviour is unintelligible to humans. The goal of the thesis is to present the importance of interpretability of machine learning models and evaluate the performance of simple models that are inherently interpretable. We tested RiskSLIM, an algorithm for building sparse linear integer models that are easy to use and compared it to more popular machine learning methods. Results were obtained on binary and multiclass medical datasets of different sizes. The performance of RiskSLIM models on binary datasets was slightly worse than performance of other methods, but very good nonetheless. RiskSLIM offers an excellent trade-off between model interpretability and classification accuracy. However, it has poor performance on datasets, where a large number of features is required for successful classification, which is not possible with RiskSLIM, as it is limited to a small number of features per model. It can also be utilized on multiclass datasets using meta-classifiers. Its major drawback is the lengthy process of building the model, which is exponentially extended on datasets with large number of features. Manual data processing is also time consuming, as it is necessary to analyze and discretize each feature individually.
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