Abstract: In some implementations, a device for generating image metadata may obtain a plurality of images associated with a plurality of vehicles. The device may generate, for each image of the plurality of images, metadata associated with the image. The device may generate the metadata in accordance with a machine learning model and in accordance with one or more characteristics associated with the image. The metadata may indicate, for example, a size of the image, a view of a vehicle included in the image, an angle of the vehicle included in the image, a quality of the image, and/or a background of the image. The device may modify the image in accordance with the metadata.

Abstract: Systems, methods, articles of manufacture, and computer program products to generate decision trees are described. In some embodiments, a computer-implemented method to generate a machine learning decision tree model may include, via at least one processor of a computing device, determining a set of numeric variables for each of the plurality of categorical variables, determining an event rate for each of the plurality of categorical variables, determining, using training data, a plurality of splits for assigning the plurality of categorical variables to nodes of the machine learning decision tree model, wherein the plurality of splits comprises a plurality of multi-categorical splits assigning multiple of the plurality of categorical variables to a single node based on the event rate, and accessing data to generate the machine learning decision tree model comprising a plurality of nodes, at least a portion of the nodes assigned one of the plurality of multi-categorical splits.

Abstract: Systems and methods for generating decision trees are described. In some embodiments, a computer-implemented method to generate a machine learning decision tree may include accessing data to generate the decision tree, the data comprising a plurality of categorical variables; determining an event rate for each of the plurality of categorical variables generating a recursive tree comprising one or more nodes for the plurality of categorical variables via, for each of the one or more nodes: determining a node split for the one or more nodes based on the event rate for each of the plurality of category variables, the node split to split each of the one or more nodes into two nodes, and determine an encoder based on the node split for each level of the recursive tree; and generating the decision tree using a plurality of encoders comprising the encoder for each level of the recursive tree.

Abstract: Methods and systems for monitoring through graphical user interfaces are disclosed. In one aspect, a system is disclosed that includes a processor and data storage including instructions that, when executed by the processor, cause the system to perform operations. The operations include maintaining an input file including predetermined criteria for a plurality of factors, receiving a model dataset generated using a model, based on the input file and the model dataset, generating a first graphical user interface that includes a graphical illustration of a subset of the plurality of factors, a model risk for the model, and a selectable feature associated with a selected factor in the subset and a selected time period. The operations further include receiving through the first graphical user interface a selection of the selectable feature, and, in response, generating a second graphical user interface that recolors a portion of the first graphical user interface.

Abstract: The exemplary embodiments may provide a search tool that can locate customer issues in call transcripts and agent notes and can provide an accurate count of how often such issues appear in the call transcripts and agent notes. The exemplary embodiments may improve the speed with which the search of documents is performed. The exemplary embodiments rely upon a document matrix that is computed once for a given corpus of documents and a given vocabulary of the documents. The document matrix may be used across multiple queries. The exemplary embodiments also account for similar terms in processing a query. The exemplary embodiments may use a word coverage factor to improve the relevance of the search results returned by the search tool. The word coverage factor acts as a multiple factor that computes the fraction a query terms that are present in a document.

Abstract: Methods and systems for monitoring through graphical user interfaces are disclosed. In one aspect, a system is disclosed that includes a processor and data storage including instructions that, when executed by the processor, cause the system to perform operations. The operations include maintaining an input file including predetermined criteria for a plurality of factors, receiving a model dataset generated using a model, based on the input file and the model dataset, generating a first graphical user interface that includes a graphical illustration of a subset of the plurality of factors, a model risk for the model, and a selectable feature associated with a selected factor in the subset and a selected time period. The operations further include receiving through the first graphical user interface a selection of the selectable feature, and, in response, generating a second graphical user interface that recolors a portion of the first graphical user interface.

Abstract: Methods and systems for monitoring through graphical user interfaces are disclosed. In one aspect, a system is disclosed that includes a processor and data storage including instructions that, when executed by the processor, cause the system to perform operations. The operations include maintaining an input file including predetermined criteria for a plurality of factors, receiving a model dataset generated using a model, based on the input file and the model dataset, generating a first graphical user interface that includes a graphical illustration of a subset of the plurality of factors, a model risk for the model, and a selectable feature associated with a selected factor in the subset and a selected time period. The operations further include receiving through the first graphical user interface a selection of the selectable feature, and, in response, generating a second graphical user interface that recolors a portion of the first graphical user interface.

Abstract: Some embodiments of the present application include obtaining datasets including a plurality of features and computing a correlation score between each of the features. Based on the correlation scores, the features may be clustered together such that each cluster includes features that are correlated with one another, and features included in different feature clusters lack correlation with one another. A machine learning model may be selected based on a set of input features for the model and the plurality of clusters such that each input feature is included in one of the feature clusters and no feature cluster includes more than one of the input features. Datasets may then be selected based on the set of input features, which may be used to generate training data for training the machine learning model.

Abstract: Methods and systems for monitoring through graphical user interfaces are disclosed. In one aspect, a system is disclosed that includes a processor and data storage including instructions that, when executed by the processor, cause the system to perform operations. The operations include maintaining an input file including predetermined criteria for a plurality of factors, receiving a model dataset generated using a model, based on the input file and the model dataset, generating a first graphical user interface that includes a graphical illustration of a subset of the plurality of factors, a model risk for the model, and a selectable feature associated with a selected factor in the subset and a selected time period. The operations further include receiving through the first graphical user interface a selection of the selectable feature, and, in response, generating a second graphical user interface that recolors a portion of the first graphical user interface.

Abstract: Methods and systems for monitoring through graphical user interfaces are disclosed. In one aspect, a system is disclosed that includes a processor and data storage including instructions that, when executed by the processor, cause the system to perform operations. The operations include maintaining an input file including predetermined criteria for a plurality of factors, receiving a model dataset generated using a model, based on the input file and the model dataset, generating a first graphical user interface that includes a graphical illustration of a subset of the plurality of factors, a model risk for the model, and a selectable feature associated with a selected factor in the subset and a selected time period. The operations further include receiving through the first graphical user interface a selection of the selectable feature, and, in response, generating a second graphical user interface that recolors a portion of the first graphical user interface.

Abstract: Methods and systems for monitoring through graphical user interfaces are disclosed. In one aspect, a system is disclosed that includes a processor and data storage including instructions that, when executed by the processor, cause the system to perform operations. The operations include maintaining an input file including predetermined criteria for a plurality of factors, receiving a model dataset generated using a model, based on the input file and the model dataset, generating a first graphical user interface that includes a graphical illustration of a subset of the plurality of factors, a model risk for the model, and a selectable feature associated with a selected factor in the subset and a selected time period. The operations further include receiving through the first graphical user interface a selection of the selectable feature, and, in response, generating a second graphical user interface that recolors a portion of the first graphical user interface.

Abstract: Methods and systems for monitoring through graphical user interfaces are disclosed. In one aspect, a system is disclosed that includes a processor and data storage including instructions that, when executed by the processor, cause the system to perform operations. The operations include maintaining an input file including predetermined criteria for a plurality of factors, receiving a model dataset generated using a model, based on the input file and the model dataset, generating a first graphical user interface that includes a graphical illustration of a subset of the plurality of factors, a model risk for the model, and a selectable feature associated with a selected factor in the subset and a selected time period. The operations further include receiving through the first graphical user interface a selection of the selectable feature, and, in response, generating a second graphical user interface that recolors a portion of the first graphical user interface.

Abstract: Methods and systems for monitoring through graphical user interfaces are disclosed. In one aspect, a system is disclosed that includes a processor and data storage including instructions that, when executed by the processor, cause the system to perform operations. The operations include maintaining an input file including predetermined criteria for a plurality of factors, receiving a model dataset generated using a model, based on the input file and the model dataset, generating a first graphical user interface that includes a graphical illustration of a subset of the plurality of factors, a model risk for the model, and a selectable feature associated with a selected factor in the subset and a selected time period. The operations further include receiving through the first graphical user interface a selection of the selectable feature, and, in response, generating a second graphical user interface that recolors a portion of the first graphical user interface.