Abstract: Methods and systems for analyzing machine-learned classifiers are disclosed herein. The method can include inputting a data item for processing by a machine-learned classifier model and receiving a plurality of confidence scores for a plurality of respective classes, the plurality of confidence scores having been generated by the machine-learned classifier model based on the data item. The method can also include determining a distance in dependence on a highest confidence score that is generated for the data item, and causing display of a class distribution diagram, where the class distribution diagram can illustrate a graphical representation corresponding to the data item located at the distance between the graphical representation of a first class and the graphical representation of a second class.

Abstract: Methods and systems for analyzing machine-learned classifiers are disclosed herein. The method can include inputting a data item for processing by a machine-learned classifier model and receiving a plurality of confidence scores for a plurality of respective classes, the plurality of confidence scores having been generated by the machine-learned classifier model based on the data item. The method can also include determining a distance in dependence on a highest confidence score that is generated for the data item, and causing display of a class distribution diagram, where the class distribution diagram can illustrate a graphical representation corresponding to the data item located at the distance between the graphical representation of a first class and the graphical representation of a second class.

Abstract: Methods and systems for analyzing machine-learned classifiers are disclosed herein. The method can include inputting a data item for processing by a machine-learned classifier model and receiving a plurality of confidence scores for a plurality of respective classes, the plurality of confidence scores having been generated by the machine-learned classifier model based on the data item. The method can also include determining a distance in dependence on a highest confidence score that is generated for the data item, and causing display of a class distribution diagram, where the class distribution diagram can illustrate a graphical representation corresponding to the data item located at the distance between the graphical representation of a first class and the graphical representation of a second class.

Abstract: Methods and systems for analyzing machine-learned classifiers are disclosed herein. The method can include inputting a data item for processing by a machine-learned classifier model and receiving a plurality of confidence scores for a plurality of respective classes, the plurality of confidence scores having been generated by the machine-learned classifier model based on the data item. The method can also include determining a distance in dependence on a highest confidence score that is generated for the data item, and causing display of a class distribution diagram, where the class distribution diagram can illustrate a graphical representation corresponding to the data item located at said distance between the graphical representation of a first class and the graphical representation of a second class.

Abstract: A method, apparatus and computer-readable instructions are described including obtaining a plurality of sets of training and test data packages (wherein: each of the plurality of sets comprises test data and training data, wherein the test data comprises a subset of a first plurality of data items and the training data comprises a remainder of the first plurality of data items; each data item of the first plurality of data items is allocated as test data for only one of the plurality of sets of training and test data packages; and each data item comprises a classification identifier); receiving a new data item having a classification identifier; and adding the new data item to one of the plurality of sets of training and test data packages as test data and adding the new data item to the other of the plurality of sets of training and test data packages as training data, wherein the one of the plurality of sets of training and test data packages to which the new data item is added as test data is selected depe

Abstract: Methods and systems for analyzing machine-learned classifiers are disclosed herein. The method can include inputting a data item for processing by a machine-learned classifier model and receiving a plurality of confidence scores for a plurality of respective classes, the plurality of confidence scores having been generated by the machine-learned classifier model based on the data item. The method can also include determining a distance in dependence on a highest confidence score that is generated for the data item, and causing display of a class distribution diagram, where the class distribution diagram can illustrate a graphical representation corresponding to the data item located at said distance between the graphical representation of a first class and the graphical representation of a second class.