A computing device receives a training data set that includes a plurality of positive examples of sensitive data and a plurality of negative examples of sensitive data. The computing device analyzes the training data set using machine learning to generate a machine learning-based detection (MLD) profile that can be used to classify new data as sensitive data or as non-sensitive data. The computing device computes a quality metric for the MLD profile.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method comprising: receiving, by a processing device, a training data set that includes a plurality of positive examples of sensitive data and a plurality of negative examples of sensitive data; analyzing, by the processing device, the training data set using machine learning to train a machine learning-based detection (MLD) profile, the MLD profile to be used to classify new data as sensitive data or as non-sensitive data; and computing, by the processing device, a quality metric for the MLD profile. 2. The method of claim 1, wherein: analyzing the training data set comprises performing feature extraction on the training data set to generate a feature set that comprises features of the positive examples and features of the negative examples and generating a classification model from the training data set; and the quality metric comprises at least one of a false positive rating, a false negative rating or a memory utilization rating. 3. The method of claim 2, further comprising: receiving a user selection of a memory allocation before analyzing the training data set, wherein the memory utilization rating complies with the memory allocation. 4. The method of claim 2, further comprising: enabling a deploy operation if the false positive rating is within a false positive threshold and the false negative rating is within a false negative threshold; receiving a user request to perform the deploy operation; and in response to receiving the user request, adding the MLD profile to a data loss prevention (DLP) policy of a DLP system. 5. The method of claim 2, further comprising: identifying at least one of data that caused false positives and data that caused false negatives from the training data set. 6. The method of claim 1, further comprising: receiving categorization information for the positive examples of sensitive data and for the negative examples of sensitive data; and identifying categories of data to add to the training data set to improve the quality metric. 7. The method of claim 1, further comprising: modifying the training data set in response to user input if the quality metric fails to meet a quality threshold; analyzing the modified training data set to retrain the MLD profile; and computing a new quality metric for the MLD profile. 8. The method of claim 1, wherein the training data set was collected by a data loss prevention (DLP) policy of a DLP system, the plurality of negative examples of sensitive data comprising documents that were misclassified as sensitive documents by the DLP policy, the method further comprising: deploying the MLD profile to the DLP policy. 9. The method of claim 1, further comprising: receiving a selection of a sensitivity threshold for the MLD profile; and controlling a sensitivity threshold setting for the MLD profile based on the selection. 10. A non-transitory computer readable storage medium including instructions that, when executed by a processing device, cause the processing device to perform operations comprising: receiving, by the processing device, a training data set that includes a plurality of positive examples of sensitive data and a plurality of negative examples of sensitive data; analyzing, by the processing device, the training data set using machine learning to train a machine learning-based detection (MLD) profile, the MLD profile to be used to classify new data as sensitive data or as non-sensitive data; and computing, by the processing device, a quality metric for the MLD profile. 11. The non-transitory computer readable storage medium of claim 10, wherein: analyzing the training data set comprises performing feature extraction on the training data set to generate a feature set that comprises features of the positive examples and features of the negative examples and generating a classification model from the training data set; and the quality metric comprises at least one of a false positive rating, a false negative rating or a memory utilization rating. 12. The non-transitory computer readable storage medium of claim 11, the operations further comprising: receiving a user selection of a memory allocation before analyzing the training data set, wherein the memory utilization rating complies with the memory allocation. 13. The non-transitory computer readable storage medium of claim 11, the operations further comprising: enabling a deploy operation if the false positive rating is within a false positive threshold and the false negative rating is within a false negative threshold; receiving a user request to perform the deploy operation; and in response to receiving the user request, adding the MLD profile to a data loss prevention (DLP) policy of a DLP system. 14. The non-transitory computer readable storage medium of claim 11, the operations further comprising: identifying at least one of data that caused false positives and data that caused false negatives from the training data set. 15. The non-transitory computer readable storage medium of claim 10, the operations further comprising: receiving categorization information for the positive examples of sensitive data and for the negative examples of sensitive data; and identifying categories of data to add to the training data set to improve the quality metric. 16. The non-transitory computer readable storage medium of claim 10, the operations further comprising: modifying the training data set in response to user input if the quality metric fails to meet a quality threshold; analyzing the modified training data set to retrain the MLD profile; and computing a new quality metric for the MLD profile. 17. The non-transitory computer readable storage medium of claim 10, wherein the training data set was collected by a data loss prevention (DLP) policy of a DLP system, the plurality of negative examples of sensitive data comprising documents that were misclassified as sensitive documents by the DLP policy, the operations further comprising: deploying the MLD profile to the DLP policy. 18. The non-transitory computer readable storage medium of claim 10, the operations further comprising: receiving a selection of a sensitivity threshold for the MLD profile; and controlling a sensitivity threshold setting for the MLD profile based on the selection. 19. A computing device comprising: a memory to store instructions for a machine learning manager; and a processing device to execute the instructions, wherein the instructions cause the processing device to: receive a training data set that includes a plurality of positive examples of sensitive data and a plurality of negative examples of sensitive data; analyze the training data set using machine learning to train a machine learning-based detection (MLD) profile to be used to classify new data as sensitive data or as non-sensitive data; and compute a quality metric for the MLD profile. 20. The computing device of claim 19, wherein analyzing the training data set comprises: performing feature extraction on the training data set to generate a feature set that comprises features of the positive examples and features of the negative examples; generating a classification model from the training data set; and wherein the quality metric comprises at least one of a false positive rating, a false negative rating or a memory utilization rating.