With the help of an electroencephalogram (EEG) the electrical activity of the brain is measured, and this can help identify chronic neurological disorders such as epilepsy. Epileptic episodes are detected by monitoring patients in order to provide preventive measures. Current research studies are using a combination of time and frequency features to recognize epileptic seizures automatically. In order to automatically detect epileptic seizures, different machine learning approaches have been used. Gradient boosting decision tree (GBDT) is a machine learning technique that is known for its efficiency, accuracy, and interpretability. In terms of performance of GBDT, many machine learning tasks such as multi-class classification, learning to rank, etc. have reported competitive performance. In this paper, epileptic seizure recognition data is investigated and split into a binary and multi-class data set for which the GBDT method is applied. In addition, the SHAP (Shapley Additive Explanations) method is used as an explanation tool to interpret the machine learning models that are produced via training for both the binary and the multi-class data set.