Principal component analysis (PCA) and partial least squares (PLS) are particularly popular as they are easy to implement and capable of tackling a variety of problems, e.g. reduce the dimensionality of the data, extra key features and detect outliers. Hence, it is not surprising that these algorithms make up for the majority of applied ML models that are described in literature . In combination with a regression model, reliable online predictions with high-dimensional data become available . Often used regression models include support vector machines (SVMs) and models based on decision trees (e.g. gradient boosting regressor (GBR), ADABoost or random forest) , , . The advantages of these regression tree ensembles include fast training times and the ability to handle large amounts of data, while providing good accuracy due to combination of multiple estimators . This decision is also made to facilitate the integration of adaptive solutions in future, which would require repeated training of new models, e.g.via recursive or ensemble-based methods . However, individual regression trees are usually not competitive with other methods like SVMs or neural networks  , but thanks to the low computational cost, regression trees can be combined with bagging or boosting techniques to build a group of estimators to improve predictive accuracy and control overfitting , . In bagging, each estimator is trained on a subset of data and the output of every estimator is averaged for the final prediction, e.g. random forest or extra trees regression . In boosting, “weak” estimators are trained in succession on a subset of data and combined into a single “strong” estimator. This can be achieved e.g. by weighting the weak estimators according to their accuracy (AdaBoost) or by fitting the weak estimators using an arbitrary loss function (gradient boosting) . Clustering is another popular method that is used to organize unlabeled data according to their similarity. Combined with PCA it is a common method for process monitoring and process fault detection .