Fair Isaac Corporation

Andrew Flint

Jari Koister

Arash Nourian

Longfei Fan

Feier Lian

Kevin Griest

Bruce Hoadley

Larry E Rosenberger

Chong Huang

Stuart L Crawford

Michael Steele

Xing Zhao

Andrew K Story

Peter Hamilton

Keith Owen Hillestad

Thomas J Quinn

Richard Spjut

Michael William Rapaport

Parama Dutta

Sue Ann Simon

Cheryl Lynne St John

Sharon Anne Hatcher

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.

Managing missing values in datasets for machine learning models

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.

Meaningfully explaining black-box machine learning models

Providing insights about a dynamic machine learning model

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.

Scorecard models with measured variable interactions

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.

Method and apparatus for explaining credit scores

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

Algorithm for explaining credit scores

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.

Growing community of inventors

El Cerrito, CA, United States of America

Andrew Flint