In a recent study, post-hoc model interpretability methods such as BreakDown (BD) and SHapley Additive exPlanations (SHAP) were employed after building an ensemble of support vector machines used for phase classification of multi-principal element alloys. The training data consisted of 1,821 instances, each with 12 features such as mean melting point, mean enthalpy of formation, and mixing entropy. For bcc structure types, both BD and SHAP methods identified mean melting temperature and maximum atomic weight difference as dominant variables. In another study, three gradient boosting methods were analyzed: histogram gradient boosting (HGB), extreme gradient boosting (XGBoost), and gradient boosting (GB) were used to predict the adhesion strength of synthesized fibrillar dry adhesives, each characterized by 7 features such as contact area and pull-off force. For each model, three explainable machine learning techniques (SHAP, LIME, DALEX) provided both local and global contributions of each feature. The study demonstrates that explainable machine learning is a viable approach for limited datasets and limited number of features in experimental settings. Similarly to our study, in the materials science discipline, SVM and XGBoost methods are common, as they result in excellent quality models, and explainability could either be achieved with post-hoc methods or be inherent to the model, like in the case with XGBoost.