Data is received that include values that correspond to a plurality of variables. A score is then generated based on the received data and using a boosted ensemble of segmented scorecard models. The boosted ensemble of segmented scorecard models includes two or more segmented scorecard models. Subsequently, data including the score can be provided (e.g., displayed, transmitted, loaded, stored, etc.). Related apparatus, systems, techniques and articles are also described.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method comprising: receiving data comprising values corresponding a plurality of variables; generating a score based on the received data and using a boosted ensemble of segmented scorecard models, the boosted ensemble of segmented scorecard models comprising two or more segmented scorecard models; and providing data comprising the score. 2. A method as in claim 1, wherein the score ƒ is calculated by the boosted ensemble of segmented scorecard models based on: f = β * s + w 0 + ∑ i = 1 I  f i  ( x i ) , wherein s is a score derived from a previous ensemble, β is a shrinking parameter; and wherein the enhanced scorecard model optimizes β and predictor bin weights so that the score ƒ is better than the score s. 3. A method as in claim 1, wherein the providing data comprises at least one of: displaying the score, transmitting data comprising the score to a remote computing system, loading data comprising the score into memory, or storing data comprising the score. 4. A method as in claim 1, wherein at least one of the receiving, generating, and providing is implemented by at least one data processor forming part of at least one computing system. 5. A method as in claim 1, wherein the boosted ensemble of segmented scorecard models is generated by: training a first segmented scorecard model; identifying or generating a second segmented scorecard model that provides an enhanced score relative to the first segmented scorecard model; enumerating split variables and split values in the second segmented scorecard model using a segmentation search algorithm; and forming the boosted ensemble of segmented scorecard models based on both the first segmented scorecard model and the second segmented scorecard model. 6. A non-transitory computer program product storing instructions which, when executed by at least one data processor forming part of at least one computing system, results in operations comprising: receiving data comprising values corresponding a plurality of variables; generating a score based on the received data and using a boosted ensemble of segmented scorecard models, the boosted ensemble of segmented scorecard models comprising two or more segmented scorecard models; and providing data comprising the score. 7. A computer program product as in claim 6, wherein the score ƒ is calculated by the boosted ensemble of segmented scorecard models based on: f = β * s + w 0 + ∑ i = 1 I  f i  ( x i ) , wherein s is a score derived from a previous ensemble, β is a shrinking parameter; and wherein the enhanced scorecard model optimizes β and predictor bin weights so that the score ƒ is better than the score s. 8. A computer program product as in claim 6, wherein the providing data comprises at least one of: displaying the score, transmitting data comprising the score to a remote computing system, loading data comprising the score into memory, or storing data comprising the score. 9. A computer program product as in claim 6, wherein the boosted ensemble of segmented scorecard models is generated by: training a first segmented scorecard model; identifying or generating a second segmented scorecard model that provides an enhanced score relative to the first segmented scorecard model; enumerating split variables and split values in the second segmented scorecard model using a segmentation search algorithm; and forming the boosted ensemble of segmented scorecard models based on both the first segmented scorecard model and the second segmented scorecard model. 10. A method comprising: training a first segmented scorecard model; identifying or generating a second segmented scorecard model that provides an enhanced score relative to the first segmented scorecard model; enumerating split variables and split values in the second segmented scorecard model using a segmentation search algorithm; and forming a boosted ensemble of scorecard models based on both the first model and the second segmented scorecard model. 11. A method as in claim 10, wherein the first segmented scorecards is trained using at least one of a scorecard model, a regression, or a neural network. 12. A method as in claim 11, wherein at least one of the training, identifying, enumerating, and forming is implemented by at least one data processor forming part of at least one computing system. 13. A method as in claim 10, further comprising: enabling local or remote access to the boosted ensemble of segmented scorecard models to enable scores to be generated.