PATENT DOCUMENT

Publication Number: US-11580444-B2
Application Number: US-201916530855-A
Country: US
Kind Code: B2

Title: Data visualization machine learning model performance

Abstract:
The subject technology receives information associated with a machine learning model. The subject technology determines a set of metrics based at least in part on the information associated with the machine learning model, where the set of metrics corresponds to respective indicators of performance of the machine learning model based on input data from a data set, the set of metrics further including a number of errors produced by the machine learning model when applied to the input data from the data set. Further, the subject technology displays a user interface based at least in part on the set of metrics, where the user interface includes a set of graphical elements, and the set of graphical elements further includes representations of the set of metrics, and representations of the input data from the data set utilized by the machine learning model.

Claims:
What is claimed is: 
     
       1. A method comprising:
 receiving information associated with a machine learning model, wherein the information comprises output data of the machine learning model, and the machine learning model is applied on input data from a data set to generate the output data; 
 determining a set of metrics based at least in part on the information associated with the machine learning model, wherein the set of metrics corresponds to respective indicators of performance of the machine learning model based on the input data from the data set, the set of metrics further including a number of errors produced by the machine learning model when applied to the input data from the data set; 
 displaying a user interface based at least in part on the set of metrics, wherein the user interface comprises a set of graphical elements, and the set of graphical elements further includes representations of the set of metrics, and representations of the input data from the data set utilized by the machine learning model; 
 displaying a first graphical element indicating a number of incorrect classifications and a set of images associated with the number of incorrect classifications in response to a selection of a particular graphical element of the representations of the input data; 
 receiving a particular selection of a particular image from the set of images; 
 receiving input corresponding to a different label for the particular image; 
 storing information related to the different label for the particular image into an updated training data set; and 
 retraining the machine learning model using at least the updated training data set. 
 
     
     
       2. The method of  claim 1 , further comprising:
 receiving a selection of the particular graphical element of the user interface, the particular graphical element corresponding to a name of a class classified by the machine learning model; and 
 displaying, in response to the selection of the particular graphical element, a second set of graphical elements, the second set of graphical elements including a second graphical element indicating an actual class of a particular input data, a third graphical element indicating a predicted class of the particular input data, the first graphical element indicating the number of incorrect classifications, and a fourth graphical element indicating the set of images associated with the number of incorrect classifications. 
 
     
     
       3. The method of  claim 2 , further comprising:
 receiving a second selection of the third graphical element indicating the predicted class; and 
 displaying, in response to the second selection of the third graphical element, a second set of images, the second set of images comprising a different version of the set of images associated with the number of incorrect classifications. 
 
     
     
       4. The method of  claim 3 , wherein the different version of the set of images comprises images with a greater resolution than corresponding resolutions of the set of images. 
     
     
       5. The method of  claim 2 , further comprising:
 receiving a particular selection of a particular image from the set of images associated with the number of incorrect classifications; and 
 displaying, in response to the particular selection, a different version of the particular image. 
 
     
     
       6. The method of  claim 1 , further comprising:
 receiving a selection of a particular graphical element of the user interface, the particular graphical element corresponding for an option for displaying additional metrics from the set of metrics; and 
 displaying, in response to the selection of the particular graphical element, a second set of graphical elements, the second set of graphical elements including additional metrics associated with the machine learning model that are not currently displayed in the user interface. 
 
     
     
       7. The method of  claim 6 , further comprising:
 receiving another selection of another particular graphical element from the second set of graphical elements, the other particular graphical element corresponding to an additional metric of the machine learning model; and 
 displaying, in response to the other selection of the other particular graphical element, a representation of the additional metric of the machine learning model in the user interface. 
 
     
     
       8. The method of  claim 1 , wherein the set of graphical elements are provided for display in a tabular format. 
     
     
       9. The method of  claim 1 , wherein the set of metrics is based at least in part on a confusion matrix. 
     
     
       10. A system comprising:
 a processor; 
 a memory device containing instructions, which when executed by the processor cause the processor to: 
 receive information associated with a machine learning model, wherein the information comprises output data of the machine learning model, and the machine learning model is applied on input data from a data set to generate the output data; 
 determine a set of metrics based at least in part on the information associated with the machine learning model, wherein the set of metrics corresponds to respective indicators of performance of the machine learning model based on the input data from the data set, the set of metrics further including a number of errors produced by the machine learning model when applied to the input data from the data set; 
 display a user interface based at least in part on the set of metrics, wherein the user interface comprises a set of graphical elements, and the set of graphical elements further includes representations of the set of metrics, and representations of the input data from the data set utilized by the machine learning model; 
 display a first graphical element indicating a number of incorrect classifications and a set of images associated with the number of incorrect classifications in response to a selection of a particular graphical element of the representations of the input data; 
 receive a particular selection of a particular image from the set of images; 
 receive input corresponding to a different label for the particular image; 
 store information related to the different label for the particular image into an updated training data set; and 
 retrain the machine learning model using at least the updated training data set. 
 
     
     
       11. The system of  claim 10 , wherein the memory device contains further instructions, which when executed by the processor further cause the processor to:
 receive a selection of a particular graphical element of the user interface, the particular graphical element corresponding to a name of a class classified by the machine learning model; and 
 display, in response to the selection of the particular graphical element, a second set of graphical elements, the second set of graphical elements including a second graphical element indicating an actual class of a particular input data, a third graphical element indicating a predicted class of the particular input data, the first graphical element indicating the number of incorrect classifications, and a fourth graphical element indicating the set of images associated with the number of incorrect classifications. 
 
     
     
       12. The system of  claim 11 , wherein the memory device contains further instructions, which when executed by the processor further cause the processor to:
 receive a second selection of the third graphical element indicating the predicted class; and 
 display, in response to the second selection of the third graphical element, a second set of images, the second set of images comprising a different version of the set of images associated with the number of incorrect classifications. 
 
     
     
       13. The system of  claim 12 , wherein the different version of the set of images comprises images with a greater resolution than corresponding resolutions of the set of images. 
     
     
       14. The system of  claim 11 , wherein the memory device contains further instructions, which when executed by the processor further cause the processor to:
 receive a particular selection of a particular image from the set of images associated with the number of incorrect classifications; and
 display, in response to the particular selection, a different version of the particular image. 
 
 
     
     
       15. The system of  claim 10 , wherein the memory device contains further instructions, which when executed by the processor further cause the processor to:
 receive a selection of a particular graphical element of the user interface, the particular graphical element corresponding for an option for displaying additional metrics from the set of metrics; and 
 display, in response to the selection of the particular graphical element, a second set of graphical elements, the second set of graphical elements including additional metrics associated with the machine learning model that are not currently displayed in the user interface. 
 
     
     
       16. The system of  claim 15 , wherein the memory device contains further instructions, which when executed by the processor further cause the processor to:
 receive another selection of another particular graphical element from the second set of graphical elements, the other particular graphical element corresponding to an additional metric of the machine learning model; and 
 display, in response to the other selection of the other particular graphical element, a representation of the additional metric of the machine learning model in the user interface. 
 
     
     
       17. The system of  claim 10 , wherein the machine learning model comprises an image classifier model and the input data comprises different images. 
     
     
       18. A non-transitory computer-readable medium comprising instructions, which when executed by a computing device, cause the computing device to perform operations comprising:
 receiving information associated with a machine learning model, wherein the information comprises output data of the machine learning model, and the machine learning model is applied on input data from a data set to generate the output data; 
 determining a set of metrics based at least in part on the information associated with the machine learning model, wherein the set of metrics corresponds to respective indicators of performance of the machine learning model based on the input data from the data set, the set of metrics further including a number of errors produced by the machine learning model when applied to the input data from the data set; 
 displaying a user interface based at least in part on the set of metrics, wherein the user interface comprises a set of graphical elements, and the set of graphical elements further includes representations of the set of metrics, and representations of the input data from the data set utilized by the machine learning model; 
 displaying a graphical element indicating a number of incorrect classifications and a set of images associated with the number of incorrect classifications in response to a selection of a particular graphical element of the representations of the input data; 
 receiving a particular selection of a particular image from the set of images; 
 receiving input corresponding to a different label for the particular image; 
 storing information related to the different label for the particular image into an updated training data set; and 
 retraining the machine learning model using at least the updated training data set. 
 
     
     
       19. The non-transitory computer-readable medium of  claim 18 , wherein the set of graphical elements are provided for display in a tabular format. 
     
     
       20. The non-transitory computer-readable medium of  claim 18 , wherein the set of metrics is based at least in part on a confusion matrix.

Description:
CROSS-REFERENCE TO RELATED APPLICATIONS 
     The present application claims the benefit of U.S. Provisional Patent Application Ser. No. 62/834,911, entitled “DATA VISUALIZATION FOR MACHINE LEARNING MODEL PERFORMANCE,” filed Apr. 16, 2019, which is hereby incorporated herein by reference in its entirety and made part of the present U.S. Utility Patent Application for all purposes. 
    
    
     TECHNICAL FIELD 
     The present description generally relates to developing machine learning applications. 
     BACKGROUND 
     Software engineers and scientists have been using computer hardware for machine learning to make improvements across different industry applications including image classification, video analytics, speech recognition and natural language processing, etc. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       Certain features of the subject technology are set forth in the appended claims. However, for purpose of explanation, several embodiments of the subject technology are set forth in the following figures. 
         FIG.  1    illustrates an example network environment for a system providing user interfaces (UIs) for viewing performance metrics for machine learning models in accordance with one or more implementations. 
         FIG.  2    illustrates an example computing architecture for a system providing user interfaces (UIs) for viewing performance metrics for machine learning models in accordance with one or more implementations. 
         FIG.  3    illustrates an example UI of an application that provides visualizations of machine learning model performance in accordance with one or more implementations. 
         FIG.  4    illustrates another example UI of an application that provides visualizations of machine learning model performance in accordance with one or more implementations. 
         FIG.  5    illustrates another example UI of an application that provides visualizations of machine learning model performance in accordance with one or more implementations. 
         FIG.  6    illustrates another example UI of an application that provides visualizations of machine learning model performance in accordance with one or more implementations. 
         FIG.  7    illustrates another example UI of an application that provides visualizations of machine learning model performance in accordance with one or more implementations. 
         FIG.  8    illustrates another example of the UI described herein in accordance with one or more implementations. 
         FIG.  9    illustrates another example UI of an application that provides visualizations of machine learning model performance in accordance with one or more implementations. 
         FIG.  10    illustrates another example UI of an application that provides visualizations of machine learning model performance in accordance with one or more implementations. 
         FIG.  11    illustrates another example UI of an application that provides visualizations of machine learning model performance in accordance with one or more implementations. 
         FIG.  12    illustrates another example UI of an application that provides visualizations of machine learning model performance in accordance with one or more implementations. 
         FIG.  13    illustrates a flow diagram of an example process for providing a user interface for displaying performance metrics of a machine learning model in accordance with one or more implementations. 
         FIG.  14    illustrates an electronic system with which one or more implementations of the subject technology may be implemented. 
     
    
    
     DETAILED DESCRIPTION 
     The detailed description set forth below is intended as a description of various configurations of the subject technology and is not intended to represent the only configurations in which the subject technology can be practiced. The appended drawings are incorporated herein and constitute a part of the detailed description. The detailed description includes specific details for the purpose of providing a thorough understanding of the subject technology. However, the subject technology is not limited to the specific details set forth herein and can be practiced using one or more other implementations. In one or more implementations, structures and components are shown in block diagram form in order to avoid obscuring the concepts of the subject technology. 
     Machine learning has seen a significant rise in popularity in recent years due to the availability of massive amounts of training data, and advances in more powerful and efficient computing hardware. Machine learning may utilize models that are executed to provide predictions in particular applications (e.g., analyzing images and videos, fraud detection, etc.) among many other types of applications. 
     The subject technology provides techniques for providing graphical visualizations of information related to performance of a given machine learning model. Specifically, the subject technology provides user interfaces that present information indicating the performance of a classifier ML model. The user interfaces enable a user to visually inspect the performance of the classifier ML model by presenting various metrics associated with model and associated classification errors. Although the examples described herein relate to a classifier ML model, it is appreciated that the subject technology, in other implementations, can also provide user interfaces for viewing performance metrics of other types of machine learning models. Performance metrics as mentioned herein can refer to any type of metric that can be utilized as a measure of an aspect of a given machine learning model&#39;s performance with respect to a given training data set. 
     Implementations of the subject technology improve the computing functionality of a given electronic device by providing an efficient approach to viewing metrics associated with the performance of a given machine learning model. Prior approaches required developers to manually insert ad-hoc and/or custom code in order to view performance metrics. The subject technology therefore avoids this by advantageously providing user interfaces that can be utilized to view such performance metrics without necessitating the inclusions of such ad-hoc and/or custom code. These benefits therefore are understood as improving the computing functionality of a given electronic device, such as an end user device (which may generally have less computational and/or power resources available than, e.g., one or more cloud-based servers) by at least providing a specific combination of user experience elements thereby making it easier for a developer to evaluate the model with less ad-hoc code. 
       FIG.  1    illustrates an example network environment for a system providing user interfaces (UIs) for viewing performance metrics for machine learning models in accordance with one or more implementations. Not all of the depicted components may be used in all implementations, however, and one or more implementations may include additional or different components than those shown in the figure. Variations in the arrangement and type of the components may be made without departing from the spirit or scope of the claims as set forth herein. Additional components, different components, or fewer components may be provided. 
     The network environment  100  includes an electronic device  110 , and a server  120 . The network  106  may communicatively (directly or indirectly) couple the electronic device  110  and/or the server  120 . In one or more implementations, the network  106  may be an interconnected network of devices that may include, or may be communicatively coupled to, the Internet. For explanatory purposes, the network environment  100  is illustrated in  FIG.  1    as including the electronic device  110 , and the server  120 ; however, the network environment  100  may include any number of electronic devices and any number of servers. 
     The electronic device  110  may be, for example, desktop computer, a portable computing device such as a laptop computer, a smartphone, a peripheral device (e.g., a digital camera, headphones), a tablet device, a wearable device such as a watch, a band, and the like. In  FIG.  1   , by way of example, the electronic device  110  is depicted as a desktop computer. The electronic device  110  may be, and/or may include all or part of, the electronic system discussed below with respect to  FIG.  14   . 
     In one or more implementations, the electronic device  110  may provide a system for training a machine learning model using training data, where the trained machine learning model is subsequently deployed to the electronic device  110 . Further, the electronic device  110  may provide one or more machine learning frameworks for training machine learning models and/or developing applications using such machine learning models. In an example, such machine learning frameworks can provide various machine learning algorithms and models for different problem domains in machine learning. In an example, the electronic device  110  may include a deployed machine learning model that provides an output of data corresponding to a prediction or some other type of machine learning output. 
     The server  120  may provide a system for training a machine learning model using training data, where the trained machine learning model is subsequently deployed to the server  120 . In an implementation, the server  120  may train a given machine learning model for deployment to a client electronic device (e.g., the electronic device  110 ). The machine learning model deployed on the server  120  can then perform one or more machine learning algorithms. In an implementation, the server  120  provides a cloud service that utilizes the trained machine learning model and continually learns over time. 
       FIG.  2    illustrates an example computing architecture for a system providing user interfaces (UIs) for viewing performance metrics for machine learning models in accordance with one or more implementations. For explanatory purposes, the computing architecture is described as being provided by the electronic device  110 , such as by a processor and/or memory of the electronic device  110 ; however, the computing architecture may be implemented by any other electronic devices, such as the server  120 . Not all of the depicted components may be used in all implementations, however, and one or more implementations may include additional or different components than those shown in the figure. Variations in the arrangement and type of the components may be made without departing from the spirit or scope of the claims as set forth herein. Additional components, different components, or fewer components may be provided. 
     As illustrated, the electronic device  110  includes training data  210  for training a machine learning model. In an example, the electronic device  110  may utilize one or more machine learning algorithms that uses training data  210  for training a machine learning (ML) model  220 . In one or more implementations, the ML model  220  may be trained on another device, such as the server  120 , and then deployed to the electronic device  110 . The electronic device  110  further includes a user interface (UI) engine  230  which performs operations for providing user interfaces for viewing performance metrics of a given machine learning model (e.g., an image classifier model), which is discussed in more detail below. The UI engine  230  includes an API  235  which may be utilized by an application  240  to provide UIs for viewing one or more performance metrics based at least in part on the output data  215  of the ML model  220 , which is discussed in further detail starting with  FIG.  3   . 
     Although the UI engine  230  is illustrated as being separate from the application  240 , in an implementation, a UI engine with the same functionality as the UI engine  230  may be included as part of the application  240  thereby enabling the application  240  to perform similar or the same functions as a separate UI engine (e.g., the UI engine  230 ). 
     In an implementation, the application  240  can determine the performance metrics based at least in part on the output data  215  generated by the ML model  220 . The application  240 , for example, can automatically generate information for including in a confusion matrix based at least in part on the output data  215 . In an implementation, the application  240  can determine performance metrics using the confusion matrix corresponding to summary of prediction results on a classification problem. In an example, the confusion matrix can include information corresponding to a number of correct predictions and a number of incorrect predictions with count values (e.g., a number of occurrences) for each class. The confusion matrix, in a binary classification example, includes a table with two dimensions (“actual” and “predicted”), and sets of “classes” in both dimensions, where actual classifications are columns and predicted classifications are rows. Additional rows and columns can be added to the confusion matrix for use in classification problems with three or more class values. The confusion matrix, in an example, can include information indicating true positives, true negatives, false positives, and false negatives generated by a classifier model, which can be utilized to determine one or more performance metrics including, but not limited to, accuracy, precision, recall, specificity, F1 score, and area under a receiver operating characteristic (ROC) curve, etc. Further, the confusion matrix can be determined based at least in part on a test data set that is utilized to evaluate the ML model  220 . 
     In an implementation, the application  240 , after generating performance metrics based on the confusion matrix, can store information related to the performance metrics as part of the output data  215  thereby forgoing re-calculating such performance metrics in the future and enabling the application  240  to simply retrieve the performance metrics for display at a subsequent time. In another implementation, the UI engine  230  may determine the performance metrics in a similar manner as the application  240  and provide the performance metrics to the application  240  thereby foregoing the application  240  from having to calculate the performance metrics (e.g., the application  240  provides the functionality of displaying the performance metrics calculated and provided by the UI engine  230 ). 
     In one or more implementations, the ML model  220  may be trained and deployed on the server  120 . The server  120  may provide a web application that can be utilized by the electronic device  110  to access the UIs for viewing the performance metrics from the output data  215  of the ML model  220 . In this example, the web application can determine the performance metrics based at least in part on the output data  215  of the ML model  220  and/or by using information from a confusion matrix in a similar manner as discussed above. 
       FIG.  3    illustrates an example UI  300  of an application that provides visualizations of machine learning model performance in accordance with one or more implementations. For explanatory purposes, the application (e.g., the application  240 ) is described as executing on the electronic device  110  of  FIG.  1   ; however, the UI  300  of the application may be implemented by any other electronic device. For example, the UI  300  may be a web application that is hosted on the server  120  and accessed on the electronic device  110 . Not all of the depicted components may be used in all implementations, however, and one or more implementations may include additional or different components than those shown in the figure. Variations in the arrangement and type of the components may be made without departing from the spirit or scope of the claims as set forth herein. Additional components, different components, or fewer components may be provided. 
     The UI  300  of  FIG.  3    may be provided after a given machine learning model (e.g., the model  220 ) has undergone training using training data (e.g., the training data) and generates output data (e.g., the output data  215 ). The output data may then be provided to an UI engine (e.g., the UI engine  230 ) for providing a UI(s) with respective graphical elements that are displayed by a given application (e.g., the application  240 ). 
     The example of  FIG.  3    relates to a user interface (UI) for visualizing aspects of the performance of an image classifier model. As illustrated, the UI  300  of the application includes different areas that provide information related to the performance of a given machine learning model. As shown, the UI  300  includes information indicating a model  310  (e.g., “Resnet-50”), a number of iterations  320 , an accuracy metric  330 , and a number of images  340  that were classified by the model  310 . In the example of  FIG.  3   , the images shown in the UI  300  are included as part of a data set that the image classifier model receives as input for classifying into different classes of images. In an example, each class corresponds to a respective label that the image classifier model has attributed or tagged (e.g., as metadata) to a corresponding image from the data set. Such labels can be stored in the output data  215  and utilized in part by the UI engine  230  to extract and derive one or more performance metrics for display in the UI  300 . 
     As shown, respective graphical areas and graphical elements therein are generally provided in a tabular format in the UI  300 . In particular, the UI  300  of the application includes a graphical area  360  that includes a listing of different names of classes (e.g., labels generated by the image classifier model). As further shown, the UI  300  of the application includes the graphical area  370  for displaying images (e.g., thumbnail images) corresponding to each of the classes from the graphical area  360 . In an example, a number of images provided for display in the graphical area  370  are limited based at least on the size of the graphical area  370 . The UI  300  can provide a scrollable section that enables a user to scroll, in a horizontal direction, and view other images in a particular row in the graphical area  370 . 
     The UI  300  also includes a graphical area  380  for displaying graphical representations of accuracy metrics corresponding to each of the classes from the graphical area  360 . In an example, such accuracy metrics may be stored as part of the output data  215 , as part of the application  240 , and/or on the server  120 . The application  240  can determine the accuracy metrics based at least in part on the output data  215  produced by the ML model  220 . As mentioned above, the application  240  and/or the web application can determine performance metrics, including the accuracy metrics, using information from a confusion matrix derived at least in part on the output data  215  of the ML model  220 . 
     In the example of  FIG.  3   , the accuracy metrics are sorted in ascending order from the least accurate metric (e.g., class “shirt”) to the most accurate metric (e.g., class “trouser”) shown in the graphical area  380  for the image classifier model. Further, the UI  300  provides a graphical area  390  showing respective numbers of images that were tested for each of the classes from the graphical area  360 . 
     For providing information related to errors from the model  310 , the UI  300  includes information indicating a number of errors  350  in classification from the model  310 , which will be discussed in further detail below. The number of errors  350 , in this example, corresponds to a total number of errors for the number of iterations  320  of training the model  310  using the input data from the data set. 
       FIG.  4    illustrates another example UI of an application that provides visualizations of machine learning model performance in accordance with one or more implementations.  FIG.  4    will be discussed with reference to portions of  FIG.  3    described above. More specifically, the example of  FIG.  4    includes portions of the UI  300  discussed above in  FIG.  3    with additional graphical elements in response to user input selecting an image  410  provided in the graphical area  370 . 
     As shown in  FIG.  4   , a row corresponding to the class “shirt” has been highlighted in the UI  300  in response to the user hovering over and/or selecting the image  410 . As further shown, a second image  420  is provided in the user corresponding to the selected image  410  in the graphical area  370 . The second image  420  is a larger version (e.g., greater resolution and/or zoomed-in) of the selected image  410  to enable the user to better visually inspect the selected image. The UI  300  indicates in the graphical area  380  a corresponding accuracy metric of “60%” for the class “shirt” that includes the selected image  410 . As discussed above, performance metrics, including an accuracy metric, can be determined by the application  240  and/or the web application using information from a confusion matrix derived at least in part on the output data  215  of the ML model  220 . 
     In this example, the classification of the images performed by the classification model is the least accurate for the images in the class “shirt” such that 40% of the images were erroneously classified by the image classifier model. To enable further investigation of these classification errors, the UI  300  provides for display further information corresponding to such errors as described further below. 
     In the following discussion, the user has selected to view errors (e.g., by selecting a row  405  corresponding to the class “shirt” or by selecting the class name corresponding to the class “shirt”) for images that were misclassified and not included in the class “shirt”. In  FIG.  4   , the UI  300  highlights the row  405  in response to the selection. 
       FIG.  5    illustrates another example UI  300  of an application that provides visualizations of machine learning model performance in accordance with one or more implementations. For example, the UI  300  may be a web application that is hosted on the server  120  and accessed on the electronic device  110 .  FIG.  5    will be discussed with reference to portions of  FIG.  3    described above. More specifically, the example of  FIG.  5    includes portions of the UI  300  discussed above in  FIG.  3    with additional graphical elements showing information related to classification errors for images included in the class “shirt”. 
     The UI  300  includes a graphical area  550  including information indicating a number of errors (“5”) and a basis for the errors (“actual:shirt”). As discussed above, performance metrics, including information related to errors, can be determined by the application  240  and/or the web application using information from a confusion matrix derived at least in part on the output data  215  of the ML model  220 . In the example of  FIG.  5   , the basis for the errors in the graphical area  550  corresponds to images that were erroneously classified by the image classifier model in a different class but instead should have been classified in the class “shirt”. 
     The UI  300  further includes a graphical area  560  showing a listing of actual classes (e.g., the aforementioned basis for the errors). As mentioned above, the actual class for images corresponds to the class “shirt” which is shown in the graphical area  560 . The UI  300  further includes a graphical area  570  that shows predicted classes for the images that were considered errors by the image classifier model. 
     The UI  300  further includes a graphical area  580  showing a number of errors as respective bars. The UI  300  further includes a graphical area  590  showing respective images corresponding to erroneously classified images. 
     In an implementation, a particular image from the graphical area  590  in the UI  300  can be selected and reclassified (e.g., by correcting its label) using the UI  300 . This implementation can be utilized for correcting labels for mislabeled images in a given data set for the image classifier model. In an example, if a given data set for classification includes an image that is mislabeled (e.g., an image of a bag is mislabeled as a shirt, an image of a shirt is mislabeled as a coat, an image of a coat is mislabeled as a dress, etc.), the UI  300  enables correction of the label for that image thereby facilitating reclassification of the image during a subsequent training run of the model. For example, an image in a row corresponding to the class name “shirt” can be selected, which the user has identified as being mislabeled. Using the UI  300 , the selected image can be relabeled to the correct label e.g., based on user input selecting or entering in a different label (e.g., class name). Upon being relabeled, the relabeled image can be stored in a new training data set. The model can then be retrained, e.g., automatically, based on the new training data set with the updated label. 
     In the following discussion, the user has selected a particular image of an erroneously classified image in the graphical area  590  of the UI  300 . 
       FIG.  6    illustrates another example UI  300  of an application that provides visualizations of machine learning model performance in accordance with one or more implementations. For example, the UI  300  may be a web application that is hosted on the server  120  and accessed on the electronic device  110 .  FIG.  6    will be discussed with reference to portions of  FIG.  5    described above. More specifically, the example of  FIG.  6    includes portions the UI  300  discussed above in  FIG.  5    with an image for a selected from the graphical area  590 . 
     As shown in  FIG.  6   , the UI  300  has received a selection of an image  650 . In response to the selection, the UI  300  displays a second image  660  corresponding to the selected image  650 . In  FIG.  6   , the UI  300  also highlights a row  605  in response to the selection. 
     The second image  660 , as shown, is larger in size and included in a different portion of the UI  300  from the selected image  650 . In this manner, the UI  300  enables the user to visually inspect the image  650  with a larger image (e.g., the second image  660 ). 
     In the following discussion, the user has selected a different predicted class in the graphical area  570  of the UI  300 . 
       FIG.  7    illustrates another example UI  300  of an application that provides visualizations of machine learning model performance in accordance with one or more implementations. For example, the UI  300  may be a web application that is hosted on the server  120  and accessed on the electronic device  110 .  FIG.  7    will be discussed with reference to portions of  FIG.  5    described above. More specifically, the example of  FIG.  7    includes the same UI  300  discussed above in  FIG.  5    with a particular predicted class selected from the graphical area  570 . 
     As shown in  FIG.  7   , the UI  300  receives a selection of a predicted class  750  corresponding to a predicted class “bag”. In  FIG.  7   , the UI  300  highlights a row  705  in response to the selection. As further shown in the UI  300 , the predicted class “bag” includes five incorrect predictions with an associated set of images  760  shown in the same row  705  of the UI  300 . 
     In the following discussion, the user has selected the row  705  of the UI  300  to view different images that were erroneously classified as the predicted class “bag”. 
       FIG.  8    illustrates another example of the UI  300  described above in accordance with one or more implementations. For example, the UI  300  may be a web application that is hosted on the server  120  and accessed on the electronic device  110 .  FIG.  8    will be discussed with reference to portions of  FIG.  7    described above. More specifically, the example of  FIG.  8    includes portions of the UI  300  discussed above in  FIG.  5    and  FIG.  7    and includes an additional graphical area  860  with respective images corresponding to the set of images  760 . 
     As shown in  FIG.  8   , the UI  300 , in response to a selection of the row  705  of the UI  300  in  FIG.  7   , provides the graphical area  860 . In the graphical area  860 , a set of images are shown that correspond to the set of images  760  in  FIG.  7   . A graphical area  850  includes information indicating that the set of images in the graphical area  860  correspond to errors for the predicted class “bag”. 
     In the following discussion, the user has selected a different image in a different row of the UI  300 . 
       FIG.  9    illustrates another example UI  300  of an application that provides visualizations of machine learning model performance in accordance with one or more implementations. For example, the UI  300  may be a web application that is hosted on the server  120  and accessed on the electronic device  110 .  FIG.  9    will be discussed with reference to portions of  FIG.  8    described above. More specifically, the example of  FIG.  9    includes portions of the UI  300  discussed above in  FIG.  8    and includes an additional image corresponding to a selected image. 
     As shown in  FIG.  9   , the UI  300 , in response to a selection of an image  920  in a row  910 , provides a second image  950  corresponding to the selected image  920 . In an example, any other image from the same row  910  can be selected, and the UI  300  can provide a larger version (e.g., greater resolution and/or zoomed-in) of the selected image in this manner. 
     In the following discussion, the user has selected a particular graphical element of the UI  300  to show a set of options for displaying additional metrics for the image classifier model. 
       FIG.  10    illustrates another example UI  300  of an application that provides visualizations of machine learning model performance in accordance with one or more implementations. For example, the UI  300  may be a web application that is hosted on the server  120  and accessed on the electronic device  110 . More specifically, the example of  FIG.  10    includes additional graphical elements showing additional metrics, for the image classifier model, that have been selected for display in the UI  300 . As discussed above, performance metrics, including information related to the additional metrics shown in  FIG.  10   , can be determined by the application  240  and/or the web application using information from a confusion matrix derived at least in part on the output data  215  of the ML model  220 . 
     As shown in  FIG.  10   , the UI  300 , in response to a selection of a graphical element  1050  (e.g., an icon, etc.), provides a graphical area  1055  with a set of options  1060  (e.g., different graphical elements) including respective metrics of the image classifier model. In an example, the graphical element  1050  corresponds to a command or operation that, upon selection, results in the display of the graphical area  1055  with the set of options  1060  in the UI  300 . As further shown in this example, graphical elements (e.g., checkboxes) corresponding to respective metrics for “Accuracy”, “F1 Score”, “Precision”, and “Recall” have been selected within the set of options  1060 . As discussed above, performance metrics, including information related to the additional metrics shown in  FIG.  10   , can be determined by the application  240  and/or the web application using information from a confusion matrix derived at least in part on the output data  215  of the ML model  220 . 
     In response to the selection of the set of options  1060 , the UI  300  displays a graphical area  1070  for metrics corresponding to precision of respective classes (e.g., an indicator of how accurate the model is for predictions of actual positives among all predictions of positives), a graphical area  1080  for metrics corresponding to recall of respective classes (e.g., an indicator of how many of the actual positives were classified by the model as a true positive among all predictions classified as true positives and false negatives), and a graphical area  1090  for metrics corresponding to F1 score of respective classes (e.g., a function of precision and recall indicating a balance between precision and recall). As further shown, the UI  300  includes information of precision  1010 , recall  1020 , and F1 score  1030  for the image classifier model. 
     In the following discussion, the user has selected a particular graphical element of the UI  300  to sort metrics for the image classifier model. 
       FIG.  11    illustrates another example UI  300  of an application that provides visualizations of machine learning model performance in accordance with one or more implementations. For example, the UI  300  may be a web application that is hosted on the server  120  and accessed on the electronic device  110 .  FIG.  11    will be discussed with reference to portions of  FIG.  10    described above. More specifically, the example of  FIG.  11    includes graphical elements showing metrics, for the image classifier model, that have been sorted in response to a selection of particular graphical element. 
     As shown in  FIG.  11   , the UI  300 , in response to a selection of a graphical element  1110  corresponding to the precision metrics, displays metrics of the image classifier model in a descending order in accordance with respective values of the precision metrics. It is appreciated that other graphical elements in the UI  300  may also be selected corresponding to any other metric in order to sort the metrics in accordance to that selection. For example, a graphical element  1120  corresponding to the recall metric may be selected, and the UI  300  would then sort the metrics for display in accordance with the values of the recall metrics. 
     In the following discussion, the user has selected a particular graphical element of the UI  300  to show further information corresponding to a particular metric of the image classifier model. 
       FIG.  12    illustrates another example UI  300  of an application that provides visualizations of machine learning model performance in accordance with one or more implementations. For example, the UI  300  may be a web application that is hosted on the server  120  and accessed on the electronic device  110 .  FIG.  12    will be discussed with reference to portions of  FIG.  10    described above. More specifically, the example of  FIG.  12    includes graphical elements showing metrics, for the image classifier model, that have been sorted in response to a selection of particular graphical element. 
     As shown in  FIG.  12   , the UI  300 , in response to a selection of a graphical element  1220  corresponding to the precision metric, displays information  1210  explaining the precision metric to the user. It is appreciated that other graphical elements in the UI  300  may also be selected corresponding to any other metric in order to display information explaining the selected metric to the user. For example, a graphical element  1230  corresponding to the recall metric may be selected, and the UI  300  would then provide information for display to explain the recall metric to the user. 
       FIG.  13    illustrates a flow diagram of an example process for providing a user interface for displaying performance metrics of a machine learning model in accordance with one or more implementations. For explanatory purposes, the process  1300  is primarily described herein with reference to components of the computing architecture of  FIG.  2   , which may be executed by one or more processors of the electronic device  110  of  FIG.  1   . However, the process  1300  is not limited to the electronic device  110 , and one or more blocks (or operations) of the process  1300  may be performed by one or more other components of other suitable devices, such as by the server  120 . Further for explanatory purposes, the blocks of the process  1300  are described herein as occurring in serial, or linearly. However, multiple blocks of the process  1300  may occur in parallel. In addition, the blocks of the process  1300  need not be performed in the order shown and/or one or more blocks of the process  1300  need not be performed and/or can be replaced by other operations. 
     The application  240  receives information associated with a machine learning model, wherein the information comprises output data of the machine learning model, and the machine learning model is applied on input data from a data set to generate the output data ( 1310 ). 
     The application  240  determines a set of metrics based at least in part on the information associated with the machine learning model, wherein the set of metrics corresponds to respective indicators of performance of the machine learning model based on the input data from the data set, and the set of metrics further includes a number of errors produced by the machine learning model when applied to the input data from the data set ( 1312 ). 
     As mentioned above, the application  240 , for example, can automatically generate information for including in a confusion matrix based at least in part on the output data  215 . In an implementation, the application  240  can determine performance metrics using the confusion matrix corresponding to summary of prediction results on a classification problem. The confusion matrix, in an example, can include information indicating true positives, true negatives, false positives, and false negatives generated by a classifier model, which can be utilized to determine one or more performance metrics including, but not limited to, accuracy, precision, recall, specificity, F1 score, and area under a receiver operating characteristic (ROC) curve, etc. Further, the confusion matrix can be determined based at least in part on a test data set that is utilized to evaluate the ML model  220 . 
     The application  240  displays a user interface based at least in part on the set of metrics, wherein the user interface comprises a set of graphical elements, the set of graphical elements including representations of the set of metrics, representations of the input data from the data set utilized by the machine learning model, and an indication of the number of errors produced by the machine learning model when applied to the input data from the data ( 1314 ). 
       FIG.  14    illustrates an electronic system  1400  with which one or more implementations of the subject technology may be implemented. The electronic system  1400  can be, and/or can be a part of, the electronic device  110 , and/or the server  120  shown in  FIG.  1   . The electronic system  1400  may include various types of computer readable media and interfaces for various other types of computer readable media. The electronic system  1400  includes a bus  1408 , one or more processing unit(s)  1412 , a system memory  1404  (and/or buffer), a ROM  1410 , a permanent storage device  1402 , an input device interface  1414 , an output device interface  1406 , and one or more network interfaces  1416 , or subsets and variations thereof. 
     The bus  1408  collectively represents all system, peripheral, and chipset buses that communicatively connect the numerous internal devices of the electronic system  1400 . In one or more implementations, the bus  1408  communicatively connects the one or more processing unit(s)  1412  with the ROM  1410 , the system memory  1404 , and the permanent storage device  1402 . From these various memory units, the one or more processing unit(s)  1412  retrieves instructions to execute and data to process in order to execute the processes of the subject disclosure. The one or more processing unit(s)  1412  can be a single processor or a multi-core processor in different implementations. 
     The ROM  1410  stores static data and instructions that are needed by the one or more processing unit(s)  1412  and other modules of the electronic system  1400 . The permanent storage device  1402 , on the other hand, may be a read-and-write memory device. The permanent storage device  1402  may be a non-volatile memory unit that stores instructions and data even when the electronic system  1400  is off. In one or more implementations, a mass-storage device (such as a magnetic or optical disk and its corresponding disk drive) may be used as the permanent storage device  1402 . 
     In one or more implementations, a removable storage device (such as a floppy disk, flash drive, and its corresponding disk drive) may be used as the permanent storage device  1402 . Like the permanent storage device  1402 , the system memory  1404  may be a read-and-write memory device. However, unlike the permanent storage device  1402 , the system memory  1404  may be a volatile read-and-write memory, such as random access memory. The system memory  1404  may store any of the instructions and data that one or more processing unit(s)  1412  may need at runtime. In one or more implementations, the processes of the subject disclosure are stored in the system memory  1404 , the permanent storage device  1402 , and/or the ROM  1410 . From these various memory units, the one or more processing unit(s)  1412  retrieves instructions to execute and data to process in order to execute the processes of one or more implementations. 
     The bus  1408  also connects to the input and output device interfaces  1414  and  1406 . The input device interface  1414  enables a user to communicate information and select commands to the electronic system  1400 . Input devices that may be used with the input device interface  1414  may include, for example, alphanumeric keyboards and pointing devices (also called “cursor control devices”). The output device interface  1406  may enable, for example, the display of images generated by electronic system  1400 . Output devices that may be used with the output device interface  1406  may include, for example, printers and display devices, such as a liquid crystal display (LCD), a light emitting diode (LED) display, an organic light emitting diode (OLED) display, a flexible display, a flat panel display, a solid state display, a projector, or any other device for outputting information. One or more implementations may include devices that function as both input and output devices, such as a touchscreen. In these implementations, feedback provided to the user can be any form of sensory feedback, such as visual feedback, auditory feedback, or tactile feedback; and input from the user can be received in any form, including acoustic, speech, or tactile input. 
     Finally, as shown in  FIG.  14   , the bus  1408  also couples the electronic system  1400  to one or more networks and/or to one or more network nodes, such as the electronic device  110  shown in  FIG.  1   , through the one or more network interface(s)  1416 . In this manner, the electronic system  1400  can be a part of a network of computers (such as a LAN, a wide area network (“WAN”), or an Intranet, or a network of networks, such as the Internet. Any or all components of the electronic system  1400  can be used in conjunction with the subject disclosure. 
     One aspect of the present technology may include the gathering and use of data available from specific and legitimate sources for performing machine learning operations such as those provided in applications that utilize machine learning models (e.g., neural networks) to provide particular functionality that may be useful for users. The present disclosure contemplates that in some instances, this gathered data may include personal information data that uniquely identifies or can be used to identify a specific person. Such personal information data can include demographic data, location-based data, online identifiers, telephone numbers, email addresses, home addresses, data or records relating to a user&#39;s health or level of fitness (e.g., vital signs measurements, medication information, exercise information), date of birth, or any other personal information. 
     The present disclosure recognizes that the use of such personal information data, in the present technology, can be used to the benefit of users. For example, the personal information data can be used to perform machine learning tasks (e.g., predictions, classifications, determining similarity, detecting anomalies, etc.) that are useful to users. Accordingly, use of such personal information data enables users to have greater control of the delivered content. Further, other uses for personal information data that benefit the user are also contemplated by the present disclosure. For instance, health and fitness data may be used, in accordance with the user&#39;s preferences to provide insights into their general wellness, or may be used as positive feedback to individuals using technology to pursue wellness goals. 
     The present disclosure contemplates that those entities responsible for the collection, analysis, disclosure, transfer, storage, or other use of such personal information data will comply with well-established privacy policies and/or privacy practices. In particular, such entities would be expected to implement and consistently apply privacy practices that are generally recognized as meeting or exceeding industry or governmental requirements for maintaining the privacy of users. Such information regarding the use of personal data should be prominently and easily accessible by users, and should be updated as the collection and/or use of data changes. Personal information from users should be collected for legitimate uses only. Further, such collection/sharing should occur only after receiving the consent of the users or other legitimate basis specified in applicable law. Additionally, such entities should consider taking any needed steps for safeguarding and securing access to such personal information data and ensuring that others with access to the personal information data adhere to their privacy policies and procedures. Further, such entities can subject themselves to evaluation by third parties to certify their adherence to widely accepted privacy policies and practices. In addition, policies and practices should be adapted for the particular types of personal information data being collected and/or accessed and adapted to applicable laws and standards, including jurisdiction-specific considerations which may serve to impose a higher standard. For instance, in the US, collection of or access to certain health data may be governed by federal and/or state laws, such as the Health Insurance Portability and Accountability Act (HIPAA); whereas health data in other countries may be subject to other regulations and policies and should be handled accordingly. 
     Despite the foregoing, the present disclosure also contemplates embodiments in which users selectively block the use of, or access to, personal information data. That is, the present disclosure contemplates that hardware and/or software elements can be provided to prevent or block access to such personal information data. For example, in the case of advertisement delivery services, the present technology can be configured to allow users to select to “opt in” or “opt out” of participation in the collection of personal information data during registration for services or anytime thereafter. In another example, users can select not to provide mood-associated data for targeted content delivery services. In yet another example, users can select to limit the length of time mood-associated data is maintained or entirely block the development of a baseline mood profile. In addition to providing “opt in” and “opt out” options, the present disclosure contemplates providing notifications relating to the access or use of personal information. For instance, a user may be notified upon downloading an app that their personal information data will be accessed and then reminded again just before personal information data is accessed by the app. 
     Moreover, it is the intent of the present disclosure that personal information data should be managed and handled in a way to minimize risks of unintentional or unauthorized access or use. Risk can be minimized by limiting the collection of data and deleting data once it is no longer needed. In addition, and when applicable, including in certain health related applications, data de-identification can be used to protect a user&#39;s privacy. De-identification may be facilitated, when appropriate, by removing identifiers, controlling the amount or specificity of data stored (e.g., collecting location data at city level rather than at an address level), controlling how data is stored (e.g., aggregating data across users), and/or other methods such as differential privacy. 
     Therefore, although the present disclosure broadly covers use of personal information data to implement one or more various disclosed embodiments, the present disclosure also contemplates that the various embodiments can also be implemented without the need for accessing such personal information data. That is, the various embodiments of the present technology are not rendered inoperable due to the lack of all or a portion of such personal information data. For example, content can be selected and delivered to users based on aggregated non-personal information data or a bare minimum amount of personal information, such as the content being handled only on the user&#39;s device or other non-personal information available to the content delivery services. 
     Implementations within the scope of the present disclosure can be partially or entirely realized using a tangible computer-readable storage medium (or multiple tangible computer-readable storage media of one or more types) encoding one or more instructions. The tangible computer-readable storage medium also can be non-transitory in nature. 
     The computer-readable storage medium can be any storage medium that can be read, written, or otherwise accessed by a general purpose or special purpose computing device, including any processing electronics and/or processing circuitry capable of executing instructions. For example, without limitation, the computer-readable medium can include any volatile semiconductor memory, such as RAM, DRAM, SRAM, T-RAM, Z-RAM, and TTRAM. The computer-readable medium also can include any non-volatile semiconductor memory, such as ROM, PROM, EPROM, EEPROM, NVRAM, flash, nvSRAM, FeRAM, FeTRAM, MRAM, PRAM, CBRAM, SONOS, RRAM, NRAM, racetrack memory, FJG, and Millipede memory. 
     Further, the computer-readable storage medium can include any non-semiconductor memory, such as optical disk storage, magnetic disk storage, magnetic tape, other magnetic storage devices, or any other medium capable of storing one or more instructions. In one or more implementations, the tangible computer-readable storage medium can be directly coupled to a computing device, while in other implementations, the tangible computer-readable storage medium can be indirectly coupled to a computing device, e.g., via one or more wired connections, one or more wireless connections, or any combination thereof. 
     Instructions can be directly executable or can be used to develop executable instructions. For example, instructions can be realized as executable or non-executable machine code or as instructions in a high-level language that can be compiled to produce executable or non-executable machine code. Further, instructions also can be realized as or can include data. Computer-executable instructions also can be organized in any format, including routines, subroutines, programs, data structures, objects, modules, applications, applets, functions, etc. As recognized by those of skill in the art, details including, but not limited to, the number, structure, sequence, and organization of instructions can vary significantly without varying the underlying logic, function, processing, and output. 
     While the above discussion primarily refers to microprocessor or multi-core processors that execute software, one or more implementations are performed by one or more integrated circuits, such as ASICs or FPGAs. In one or more implementations, such integrated circuits execute instructions that are stored on the circuit itself. 
     Those of skill in the art would appreciate that the various illustrative blocks, modules, elements, components, methods, and algorithms described herein may be implemented as electronic hardware, computer software, or combinations of both. To illustrate this interchangeability of hardware and software, various illustrative blocks, modules, elements, components, methods, and algorithms have been described above generally in terms of their functionality. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the overall system. Skilled artisans may implement the described functionality in varying ways for each particular application. Various components and blocks may be arranged differently (e.g., arranged in a different order, or partitioned in a different way) all without departing from the scope of the subject technology. 
     It is understood that any specific order or hierarchy of blocks in the processes disclosed is an illustration of example approaches. Based upon design preferences, it is understood that the specific order or hierarchy of blocks in the processes may be rearranged, or that all illustrated blocks be performed. Any of the blocks may be performed simultaneously. In one or more implementations, multitasking and parallel processing may be advantageous. Moreover, the separation of various system components in the implementations described above should not be understood as requiring such separation in all implementations, and it should be understood that the described program components and systems can generally be integrated together in a single software product or packaged into multiple software products. 
     As used in this specification and any claims of this application, the terms “base station”, “receiver”, “computer”, “server”, “processor”, and “memory” all refer to electronic or other technological devices. These terms exclude people or groups of people. For the purposes of the specification, the terms “display” or “displaying” means displaying on an electronic device. 
     As used herein, the phrase “at least one of” preceding a series of items, with the term “and” or “or” to separate any of the items, modifies the list as a whole, rather than each member of the list (i.e., each item). The phrase “at least one of” does not require selection of at least one of each item listed; rather, the phrase allows a meaning that includes at least one of any one of the items, and/or at least one of any combination of the items, and/or at least one of each of the items. By way of example, the phrases “at least one of A, B, and C” or “at least one of A, B, or C” each refer to only A, only B, or only C; any combination of A, B, and C; and/or at least one of each of A, B, and C. 
     The predicate words “configured to”, “operable to”, and “programmed to” do not imply any particular tangible or intangible modification of a subject, but, rather, are intended to be used interchangeably. In one or more implementations, a processor configured to monitor and control an operation or a component may also mean the processor being programmed to monitor and control the operation or the processor being operable to monitor and control the operation. Likewise, a processor configured to execute code can be construed as a processor programmed to execute code or operable to execute code. 
     Phrases such as an aspect, the aspect, another aspect, some aspects, one or more aspects, an implementation, the implementation, another implementation, some implementations, one or more implementations, an embodiment, the embodiment, another embodiment, some implementations, one or more implementations, a configuration, the configuration, another configuration, some configurations, one or more configurations, the subject technology, the disclosure, the present disclosure, other variations thereof and alike are for convenience and do not imply that a disclosure relating to such phrase(s) is essential to the subject technology or that such disclosure applies to all configurations of the subject technology. A disclosure relating to such phrase(s) may apply to all configurations, or one or more configurations. A disclosure relating to such phrase(s) may provide one or more examples. A phrase such as an aspect or some aspects may refer to one or more aspects and vice versa, and this applies similarly to other foregoing phrases. 
     The word “exemplary” is used herein to mean “serving as an example, instance, or illustration”. Any embodiment described herein as “exemplary” or as an “example” is not necessarily to be construed as preferred or advantageous over other implementations. Furthermore, to the extent that the term “include”, “have”, or the like is used in the description or the claims, such term is intended to be inclusive in a manner similar to the term “comprise” as “comprise” is interpreted when employed as a transitional word in a claim. 
     All structural and functional equivalents to the elements of the various aspects described throughout this disclosure that are known or later come to be known to those of ordinary skill in the art are expressly incorporated herein by reference and are intended to be encompassed by the claims. Moreover, nothing disclosed herein is intended to be dedicated to the public regardless of whether such disclosure is explicitly recited in the claims. No claim element is to be construed under the provisions of 35 U.S.C. § 112, sixth paragraph, unless the element is expressly recited using the phrase “means for” or, in the case of a method claim, the element is recited using the phrase “step for”. 
     The previous description is provided to enable any person skilled in the art to practice the various aspects described herein. Various modifications to these aspects will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other aspects. Thus, the claims are not intended to be limited to the aspects shown herein, but are to be accorded the full scope consistent with the language claims, wherein reference to an element in the singular is not intended to mean “one and only one” unless specifically so stated, but rather “one or more”. Unless specifically stated otherwise, the term “some” refers to one or more. Pronouns in the masculine (e.g., his) include the feminine and neuter gender (e.g., her and its) and vice versa. Headings and subheadings, if any, are used for convenience only and do not limit the subject disclosure.

Metadata:
Filing Date: 20190802
Publication Date: 20230214
Grant Date: 20230214
Priority Date: 20190416
Inventors: FRANKLIN, AARON B.
WONGSUPHASAWAT, Kanit
PRATAPA, NAGA RAMA ABHISHEK
SRIDHAR, SRIKRISHNA
NATION, ZACHARY A.
Assignee: APPLE INC
CPC Classifications: [{"code": "G06F16/904", "inventive": true, "first": true, "tree": "[]"}, {"code": "G06F16/904", "inventive": true, "first": false, "tree": "[]"}, {"code": "G06N20/00", "inventive": true, "first": true, "tree": "[]"}, {"code": "G06N3/08", "inventive": true, "first": false, "tree": "[]"}, {"code": "G06N20/00", "inventive": true, "first": false, "tree": "[]"}, {"code": "G06F17/16", "inventive": true, "first": false, "tree": "[]"}, {"code": "G06N20/00", "inventive": true, "first": true, "tree": "[]"}, {"code": "G06F16/904", "inventive": true, "first": false, "tree": "[]"}, {"code": "G06F17/16", "inventive": true, "first": false, "tree": "[]"}]
Family ID: 72832612