User interface and workflow for performing machine learning
First Claim
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1. A method comprising:
- receiving a training data set that includes a plurality of positive examples of sensitive data and a plurality of negative examples of sensitive data via a user interface;
analyzing 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;
displaying a quality metric for the MLD profile in the user interface; and
if the quality metric fails to meet a quality threshold, performing the following comprising;
modifying the training data set in response to user input; and
analyzing the modified training data set to retain the MLD profile.
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Abstract
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 via a user interface. 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 displays a quality metric for the MLD profile in the user interface.
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Citations
23 Claims
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1. A method comprising:
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receiving a training data set that includes a plurality of positive examples of sensitive data and a plurality of negative examples of sensitive data via a user interface; analyzing 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; displaying a quality metric for the MLD profile in the user interface; and if the quality metric fails to meet a quality threshold, performing the following comprising; modifying the training data set in response to user input; and analyzing the modified training data set to retain the MLD profile. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9)
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10. A non-transitory computer readable storage medium including instructions that, when executed by a processing device, cause the processing device to perform a method comprising:
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receiving a training data set that includes a plurality of positive examples of sensitive data and a plurality of negative examples of sensitive data via a user interface; analyzing 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; displaying a quality metric for the MLD profile in the user interface; and if the quality metric fails to meet a quality threshold, performing the following comprising; modifying the training data set in response to user input; and analyzing the modified training data set to retrain the MLD profile. - View Dependent Claims (11, 12, 13, 14, 15, 16, 17, 18)
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19. A computing device comprising:
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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; provide a user interface for the machine learning manager; receive a training data set that includes a plurality of positive examples of sensitive data and a plurality of negative examples of sensitive data via the user interface; 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; display a quality metric for the MLD profile in the user interface; and if the quality metric fails to meet a quality threshold, performing the following comprising; modify the training data set in response to user input; and analyze the modified training data set to retrain the MLD profile. - View Dependent Claims (20, 21, 22, 23)
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Specification