Abstract: Computer-implemented machines, systems and methods for managing missing values in a dataset for a machine learning model. The method may comprise importing a dataset with missing values; computing data statistics and identifying the missing values; verifying the missing values; updating the missing values; imputing missing values; encoding reasons for why values are missing; combining imputed missing values and the encoded reasons; and recommending models and hyperparameters to handle special or missing values.

Abstract: Computer-implemented machines, systems and methods for managing missing values in a dataset for a machine learning model. The method may comprise importing a dataset with missing values; computing data statistics and identifying the missing values; verifying the missing values; updating the missing values; imputing missing values; encoding reasons for why values are missing; combining imputed missing values and the encoded reasons; and recommending models and hyperparameters to handle special or missing values.

Abstract: Computer-implemented machines, systems and methods for providing insights about a machine learning model, the machine learning model trained, during a training phase, to learn patterns to correctly classify input data associated with risk analysis. Analyzing one or more features of the machine learning model, the one or more features being defined based on one or more constraints associated with one or more values and relationships and whether said one or more values and relationships satisfy at least one of the one or more constraints. Displaying one or more visual indicators based on an analysis of the one or more features and training data used to train the machine learning model, the one or more visual indicators providing a summary of the machine learning model's performance or efficacy.

Abstract: Computer-implemented machines, systems and methods for providing insights about a machine learning model, the machine learning model trained, during a training phase, to learn patterns to correctly classify input data associated with risk analysis. Analyzing one or more features of the machine learning model, the one or more features being defined based on one or more constraints associated with one or more values and relationships and whether said one or more values and relationships satisfy at least one of the one or more constraints. Displaying one or more visual indicators based on an analysis of the one or more features and training data used to train the machine learning model, the one or more visual indicators providing a summary of the machine learning model's performance or efficacy.

Abstract: Computer-implemented machines, systems and methods for managing missing values in a dataset for a machine learning model. The method may comprise importing a dataset with missing values; computing data statistics and identifying the missing values; verifying the missing values; updating the missing values; imputing missing values; encoding reasons for why values are missing; combining imputed missing values and the encoded reasons; and recommending models and hyperparameters to handle special or missing values.

Abstract: Computer-implemented machines, systems and methods for providing insights about a machine learning model, the machine learning model trained, during a training phase, to learn patterns to correctly classify input data associated with risk analysis. Analyzing one or more features of the machine learning model, the one or more features being defined based on one or more constraints associated with one or more values and relationships and whether said one or more values and relationships satisfy at least one of the one or more constraints. Displaying one or more visual indicators based on an analysis of the one or more features and training data used to train the machine learning model, the one or more visual indicators providing a summary of the machine learning model's performance or efficacy.

Abstract: Computer-implemented machines, systems and methods for managing missing values in a dataset for a machine learning model. The method may comprise importing a dataset with missing values; computing data statistics and identifying the missing values; verifying the missing values; updating the missing values; imputing missing values; encoding reasons for why values are missing; combining imputed missing values and the encoded reasons; and recommending models and hyperparameters to handle special or missing values.

Abstract: Computer-implemented machines, systems and methods for providing insights about a machine learning model, the machine learning model trained, during a training phase, to learn patterns to correctly classify input data associated with risk analysis. Analyzing one or more features of the machine learning model, the one or more features being defined based on one or more constraints associated with one or more values and relationships and whether said one or more values and relationships satisfy at least one of the one or more constraints. Displaying one or more visual indicators based on an analysis of the one or more features and training data used to train the machine learning model, the one or more visual indicators providing a summary of the machine learning model's performance or efficacy.

Abstract: Data is received that characterizes a transaction and includes a plurality of values corresponding to variables. Thereafter, a score is determined for the transaction based on the received data and using a scoring model. The scoring model only uses variables pairs having a divergence residual above a pre-defined threshold. Thereafter, data is provided that characterizes the determined score. Related apparatus, systems, techniques and computer program products are also described.

Abstract: Data is received that characterizes a transaction and includes a plurality of values corresponding to variables. Thereafter, a score is determined for the transaction based on the received data and using a scoring model. The scoring model only uses variables pairs having a divergence residual above a pre-defined threshold. Thereafter, data is provided that characterizes the determined score. Related apparatus, systems, techniques and computer program products are also described.

Abstract: The invention comprises a method and apparatus for explaining credit scores, for example in connection with a credit score explanation service, in which consumers can identify the sources of information used to establish their credit score, supply their credit report and credit score information in connection with their application for credit-related products and services, such as loans, and determine the effect on their credit score and cost for credit-related products and services based upon various hypothetical changes in their credit behavior.

Abstract: An exemplary Web-based score explanation service typically requires only the credit bureau identifier, credit score, and up to four reason codes as input.

Abstract: A security service of computer networks having a policy builder, an LDAP-compliant database, a validator and an API. The policy builder component provides a graphical user interface to be used by a policy manager to define access policies for users seeking to access network services and resources. The graphical user interface has a grid of nodes representing access policies. The grid is arranged to correspond to a defined tree structure representing services and resources and a business relationship tree structure representing users. The graphical user interface permits the policy manager to define policy builder plug-ins for access policy customization. The LDAP-compliant database maintains the policy builder plug-ins. The validator component receives requests from users and queries the LDAP-compliant database to obtain relevant access policies as defined by the policy manager.

Abstract: A security service of computer networks having a policy builder, an LDAP-compliant database, a validator and an API. The policy builder component provides a graphical user interface to be used by a policy manager to define access policies for users seeking to access network services and resources. The graphical user interface has a grid of nodes representing access policies. The grid is arranged to correspond to a defined tree structure representing services and resources and a business relationship tree structure representing users. The graphical user interface permits the policy manager to define policy builder plug-ins for access policy customization. The LDAP-compliant database maintains the policy builder plug-ins. The validator component receives requests from users and queries the LDAP-compliant database to obtain relevant access policies as defined by the policy manager.

Abstract: A security service of computer networks having a policy builder, an LDAP-compliant database, a validator and an API. The policy builder component provides a graphical user interface to be used by a policy manager to define access policies for users seeking to access network services and resources. The graphical user interface has a grid of nodes representing access policies. The grid is arranged to correspond to a defined tree structure representing services and resources and a business relationship tree structure representing users. The graphical user interface permits the policy manager to define policy builder plug-ins for access policy customization. The LDAP-compliant database maintains the policy builder plug-ins. The validator component receives requests from users and queries the LDAP-compliant database to obtain relevant access policies as defined by the policy manager.

Abstract: An exemplary Web-based score explanation service typically requires only the credit bureau identifier, credit score, and up to four reason codes as input.

Abstract: An exemplary Web-based score explanation service typically requires only the credit bureau identifier, credit score, and up to four reason codes as input. The invention herein discloses an algorithm that is used to provide an explanation of the primary factors influencing the score, where a rich data feed is provided to the facility implementing the algorithm.

Abstract: The invention relates in part to screening assays for identifying agents that alter the interaction between a protein tyrosine phosphatase (PTP) and its tyrosine phosphorylated polypeptide substrate, using fluorescence energy signals generated by detectably labeled substrates. Assays are provided in certain embodiments, including high throughput screening assays, wherein candidate agents are screened by fluorescence polarization for their ability to influence (i) binding of substrate trapping mutant PTPs to substrates, or (ii) dephosphorylation of tyrosine phosphorylated substrates by PTPs.

Abstract: A Web site is provided that contains an array of informative resources including for-pay services and extranet functions to serve consumers and traditional players in the financial services industry, including financial counselors, mortgage brokers, direct lenders, large national credit issuers, and third-party credit report re-sellers, plus information seekers such as the press, consumer groups, and government agencies. A primary focus is to educate consumers, consumer groups, and the consumer press by offering them access to the exceptionally high-quality information, both general and personal, about the practices of collection, storing, reporting, and evaluating consumer credit data.