Abstract: In non-limiting examples of the present disclosure, systems, methods and devices for optimizing machine-learned tree ensemble prediction systems are presented. A plurality of instances may be processed by a tree ensemble. Determinations regarding the expected output values of one or more nodes of the tree ensemble may be made based, at least in part, on the processed instances. Further determinations regarding the node contribution values of one or more nodes of the tree ensemble to downstream nodes may be made based on the node output values. In some examples, feature value ranges may be computed for one or more features of the tree ensemble. One or more tree ensemble optimization operations may be performed based on the information determined from one or more of the above-described operations.

Abstract: Examples for detecting anomalies in a dataset are provided herein. A decision tree is trained using the data set and partitions of the data set produced by the trained decision tree are identified. Further, subsets of data based at least on the partitions of the data set are identified and z-scores are computed for the subsets of data. Based at least on the subsets of data, a subset of data with a highest z-score is identified as an anomalous subset of data, and the anomalous subset of data is provided for display.

Abstract: Examples for detecting anomalies in a dataset are provided herein. A decision tree is trained using the data set and partitions of the data set produced by the trained decision tree are identified. Further, subsets of data based at least on the partitions of the data set are identified and z-scores are computed for the subsets of data. Based at least on the subsets of data, a subset of data with a highest z-score is identified as an anomalous subset of data, and the anomalous subset of data is provided for display.

Abstract: In non-limiting examples of the present disclosure, systems, methods and devices for optimizing machine-learned tree ensemble prediction systems are presented. A plurality of instances may be processed by a tree ensemble. Determinations regarding the expected output values of one or more nodes of the tree ensemble may be made based, at least in part, on the processed instances. Further determinations regarding the node contribution values of one or more nodes of the tree ensemble to downstream nodes may be made based on the node output values. In some examples, feature value ranges may be computed for one or more features of the tree ensemble. One or more tree ensemble optimization operations may be performed based on the information determined from one or more of the above-described operations.