An approach is provided in which a knowledge manager trains a machine-learning model and generates a hyperplane based upon a first set of labeled feature vectors. The knowledge manager computes, relative to the hyperplane, a first distribution of a first set of feature vectors corresponding to a first set of source documents. Subsequently, the knowledge manager computes, relative to the hyperplane, a second distribution of a second set of feature vectors corresponding to a second group of source documents. The knowledge manager, in turn, generates an indicator to retrain the machine-learning model in response to determining that a distribution difference between the second distribution and the first distribution reaches a distribution difference threshold.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method implemented by an information handling system that includes a memory and a processor, the method comprising: training a machine-learning model utilizing a first set of labeled feature vectors, wherein the training results in a first hyperplane; computing a first distribution of a first set of feature vectors relative to the first hyperplane, wherein the first set of feature vectors correspond to a first group of source documents; computing a second distribution of a second set of feature vectors relative to the first hyperplane, wherein the second set of feature vectors correspond to at least a second group of source documents; and generating an indicator in response to determining that a distribution difference between the second distribution and the first distribution reaches a distribution difference threshold, wherein the machine-learning model is retrained based on the generated indicator. 2. The method of claim 1 further comprising: labeling a portion of the second set of feature vectors, resulting in a second set of labeled feature vectors; and utilizing a combination of the first set of labeled feature vectors and the second set of labeled feature vectors to retrain the machine-learning model. 3. The method of claim 2 further comprising: generating a second hyperplane in response to the retraining of the machine-learning model, wherein the second hyperplane is different from the first hyperplane. 4. The method of claim 1 further comprising: generating the second set of feature vectors in response to ingesting the second group of source documents subsequent to the computation of the first distribution. 5. The method of claim 1 wherein the determination that the distribution difference reaches the distribution difference further comprises: computing a baseline ratio based upon a number of yes labeled baseline feature vectors and a number of no labeled baseline feature vectors within a predetermined width from the first hyperplane; computing a subsequent ratio based upon a number of yes labeled subsequent feature vectors and a number of no labeled subsequent feature vectors within the predetermined width from the first hyperplane; and determining that the difference between the subsequent ratio and the baseline ratio reaches the distribution difference threshold. 6. The method of claim 1 wherein the machine-learning model is a logistic regression model utilized by a question answer system to generate candidate answers to a question. 7. The method of claim 1 wherein the second set of feature vectors includes the first set of feature vectors. 8. An information handling system comprising: one or more processors; a memory coupled to at least one of the processors; and a set of computer program instructions stored in the memory and executed by at least one of the processors in order to perform actions of: training a machine-learning model utilizing a first set of labeled feature vectors, wherein the training results in a first hyperplane; computing a first distribution of a first set of feature vectors relative to the first hyperplane, wherein the first set of feature vectors correspond to a first group of source documents; computing a second distribution of a second set of feature vectors relative to the first hyperplane, wherein the second set of feature vectors correspond to at least a second group of source documents; and generating an indicator in response to determining that a distribution difference between the second distribution and the first distribution reaches a distribution difference threshold, wherein the machine-learning model is retrained based on the generated indicator. 9. The information handling system of claim 8 wherein the one or more processors perform additional actions comprising: labeling a portion of the second set of feature vectors, resulting in a second set of labeled feature vectors; and utilizing a combination of the first set of labeled feature vectors and the second set of labeled feature vectors to retrain the machine-learning model. 10. The information handling system of claim 9 wherein the one or more processors perform additional actions comprising: generating a second hyperplane in response to the retraining of the machine-learning model, wherein the second hyperplane is different from the first hyperplane. 11. The information handling system of claim 8 wherein the one or more processors perform additional actions comprising: generating the second set of feature vectors in response to ingesting the second group of source documents subsequent to the computation of the first distribution. 12. The information handling system of claim 8 wherein the one or more processors perform additional actions comprising: computing a baseline ratio based upon a number of yes labeled baseline feature vectors and a number of no labeled baseline feature vectors within a predetermined width from the first hyperplane; computing a subsequent ratio based upon a number of yes labeled subsequent feature vectors and a number of no labeled subsequent feature vectors within the predetermined width from the first hyperplane; and determining that the difference between the subsequent ratio and the baseline ratio reaches the distribution difference threshold. 13. The information handling system of claim 8 wherein the machine-learning model is a logistic regression model utilized by a question answer system to generate candidate answers to a question. 14. The information handling system of claim 8 wherein the second set of feature vectors includes the first set of feature vectors. 15. A computer program product stored in a computer readable storage medium, comprising computer program code that, when executed by an information handling system, causes the information handling system to perform actions comprising: training a machine-learning model utilizing a first set of labeled feature vectors, wherein the training results in a first hyperplane; computing a first distribution of a first set of feature vectors relative to the first hyperplane, wherein the first set of feature vectors correspond to a first group of source documents; computing a second distribution of a second set of feature vectors relative to the first hyperplane, wherein the second set of feature vectors correspond to at least a second group of source documents; and generating an indicator in response to determining that a distribution difference between the second distribution and the first distribution reaches a distribution difference threshold, wherein the machine-learning model is retrained based on the generated indicator. 16. The computer program product of claim 15 wherein the information handling system performs additional actions comprising: labeling a portion of the second set of feature vectors, resulting in a second set of labeled feature vectors; and utilizing a combination of the first set of labeled feature vectors and the second set of labeled feature vectors to retrain the machine-learning model. 17. The computer program product of claim 16 wherein the information handling system performs additional actions comprising: generating a second hyperplane in response to the retraining of the machine-learning model, wherein the second hyperplane is different from the first hyperplane. 18. The computer program product of claim 15 wherein the information handling system performs additional actions comprising: generating the second set of feature vectors in response to ingesting the second group of source documents subsequent to the computation of the first distribution. 19. The computer program product of claim 15 wherein the information handling system performs additional actions comprising: computing a baseline ratio based upon a number of yes labeled baseline feature vectors and a number of no labeled baseline feature vectors within a predetermined width from the first hyperplane; computing a subsequent ratio based upon a number of yes labeled subsequent feature vectors and a number of no labeled subsequent feature vectors within the predetermined width from the first hyperplane; and determining that the difference between the subsequent ratio and the baseline ratio reaches the distribution difference threshold. 20. The computer program product of claim 15 wherein the machine-learning model is a logistic regression model utilized by a question answer system to generate candidate answers to a question.