Abstract: Certain aspects produce a scoring model that can automatically classify future text samples. In some examples, a processing device perform operations for producing a scoring model using active learning. The operations includes receiving existing text samples and searching a stored, pre-trained corpus defining embedding vectors for selected words, phrases, or documents to produce nearest neighbor vectors for each embedding vector. Nearest neighbor selections are identified based on distance between each nearest neighbor vector and the embedding vector for each selection to produce a text cloud. Text samples are selected from the text cloud to produce seed data that is used to train a text classifier. A scoring model can be produced based on the text classifier. The scoring model can receive a plurality of new text samples and provide a score indicative of a likelihood of being a member of a selected class.

Abstract: In some aspects, a machine-learning model, which can transform input attribute values into a predictive or analytical output value, can be trained with training data grouped into attributes. A subset of the attributes can be selected and transformed into a transformed attribute used for training the model. The transformation can involve grouping portions of the training data for the subset of attributes into respective multi-dimensional bins. Each dimension of a multi-dimensional bin can correspond to a respective selected attribute. The transformation can also involve computing interim predictive output values. Each interim predictive output value can be generated from a respective training data portion in a respective multi-dimensional bin. The transformation can also involve computing smoothed interim output values by applying a smoothing function to the interim predictive output values.

Abstract: Certain aspects produce a scoring model that can automatically classify future text samples. In some examples, a processing device perform operations for producing a scoring model using active learning. The operations includes receiving existing text samples and searching a stored, pre-trained corpus defining embedding vectors for selected words, phrases, or documents to produce nearest neighbor vectors for each embedding vector. Nearest neighbor selections are identified based on distance between each nearest neighbor vector and the embedding vector for each selection to produce a text cloud. Text samples are selected from the text cloud to produce seed data that is used to train a text classifier. A scoring model can be produced based on the text classifier. The scoring model can receive a plurality of new text samples and provide a score indicative of a likelihood of being a member of a selected class.

Abstract: According to certain implementations, an access control system controls access to secured data that is stored on a secured source. A requestor system may request information representing the secured data. The access control system receives the secured data from the secured source, and selects a portion of the secured data based on a lens including a filter criteria or a modification instruction. Adjusted data may be generated based on a modification of the selected portion of data, where the modification is based on the lens. The access control system provides the adjusted data to the requestor system via an access interface. In some cases, upon completion of a time period, the access control system prevents the requestor system from accessing the adjusted data, by disabling the access interface used to access the adjusted data. The adjusted data may be deleted from the access control system.

Abstract: In some aspects, a machine-learning model, which can transform input attribute values into a predictive or analytical output value, can be trained with training data grouped into attributes. A subset of the attributes can be selected and transformed into a transformed attribute used for training the model. The transformation can involve grouping portions of the training data for the subset of attributes into respective multi-dimensional bins. Each dimension of a multi-dimensional bin can correspond to a respective selected attribute. The transformation can also involve computing interim predictive output values. Each interim predictive output value can be generated from a respective training data portion in a respective multi-dimensional bin. The transformation can also involve computing smoothed interim output values by applying a smoothing function to the interim predictive output values.