1. A method, implemented by a computing device, comprising: receiving a training data set that comprises a plurality of sensitive documents and a plurality of non-sensitive documents; determining, by the computing device, a quality of the training data set, wherein determining the quality of the training data set comprises performing at least one of k-fold cross validation or latent semantic indexing using the training data set; in response to determining that the training data set has a satisfactory quality, analyzing, by the computing device, the training data set using machine learning to generate a machine learning-based detection (MLD) profile, the MLD profile to be used by a data loss prevention (DLP) system to classify new documents as sensitive documents or as non-sensitive documents; and in response to determining that the training data set does not have satisfactory quality, identifying at least one document from the training data set that caused the quality of the training data set to be reduced.
1. A method, implemented by a computing device, comprising: receiving a training data set that comprises a plurality of sensitive documents and a plurality of non-sensitive documents; determining, by the computing device, a quality of the training data set, wherein determining the quality of the training data set comprises performing at least one of k-fold cross validation or latent semantic indexing using the training data set; in response to determining that the training data set has a satisfactory quality, analyzing, by the computing device, the training data set using machine learning to generate a machine learning-based detection (MLD) profile, the MLD profile to be used by a data loss prevention (DLP) system to classify new documents as sensitive documents or as non-sensitive documents; and in response to determining that the training data set does not have satisfactory quality, identifying at least one document from the training data set that caused the quality of the training data set to be reduced. 3. The method of claim 1 , further comprising: receiving a user selection of a memory allocation via a user interface before analyzing the training data set; and determining whether a memory utilization for the MLD profile complies with the memory allocation.