Transforming attributes for training automated modeling systems
First Claim
1. A system comprising:
- a processing device; and
one or more memory devices storing;
instructions executable by the processing device,a machine-learning model that is a memory structure comprising input nodes interconnected with one or more output nodes via intermediate nodes, wherein the intermediate nodes are configured to transform input attribute values into a predictive or analytical output value for an entity associated with the input attribute values, andtraining data for training the machine-learning model, wherein the training data are grouped into attributes;
wherein the processing device is configured to access the one or more memory devices and thereby execute the instructions to;
select a subset of attributes from the attributes of the training data;
transform the subset of attributes into a transformed attribute by performing operations comprising;
grouping (a) a first portion of the training data for the subset of attributes into a first multi-dimensional bin and (b) a second portion of the training data for the subset of attributes into a second multi-dimensional bin, wherein a dimension for each multi-dimensional bin corresponds to an attribute range of a respective one of the attributes in the subset of attributes,computing a first set of interim predictive output values for a first attribute in the subset of attributes, wherein the first set of interim predictive output values is generated from a first subset of the training data within a first range of the attribute ranges,computing a first set of smoothed interim output values by applying a smoothing function to the first set of interim predictive output values,computing a second set of interim predictive output values for a second attribute in the subset of attributes, wherein the second set of interim predictive output values is generated from a second subset of the training data within a second range of the attribute ranges,computing a second set of smoothed interim output values by applying the smoothing function to the second set of interim predictive output values, andoutputting a dataset for the transformed attribute, the dataset having, at least, a first dimension including the first set of smoothed interim output values and a second dimension including the second set of smoothed interim output values; and
train the machine-learning model with the transformed attribute.
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Abstract
In some aspects, a machine-learning model, which can transform input attribute values into a predictive or analytical output value, can be trained with training data grouped into attributes. A subset of the attributes can be selected and transformed into a transformed attribute used for training the model. The transformation can involve grouping portions of the training data for the subset of attributes into respective multi-dimensional bins. Each dimension of a multi-dimensional bin can correspond to a respective selected attribute. The transformation can also involve computing interim predictive output values. Each interim predictive output value can be generated from a respective training data portion in a respective multi-dimensional bin. The transformation can also involve computing smoothed interim output values by applying a smoothing function to the interim predictive output values. The transformation can also involve outputting the smoothed interim output values as a dataset for the transformed attribute.
28 Citations
20 Claims
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1. A system comprising:
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a processing device; and one or more memory devices storing; instructions executable by the processing device, a machine-learning model that is a memory structure comprising input nodes interconnected with one or more output nodes via intermediate nodes, wherein the intermediate nodes are configured to transform input attribute values into a predictive or analytical output value for an entity associated with the input attribute values, and training data for training the machine-learning model, wherein the training data are grouped into attributes; wherein the processing device is configured to access the one or more memory devices and thereby execute the instructions to; select a subset of attributes from the attributes of the training data; transform the subset of attributes into a transformed attribute by performing operations comprising; grouping (a) a first portion of the training data for the subset of attributes into a first multi-dimensional bin and (b) a second portion of the training data for the subset of attributes into a second multi-dimensional bin, wherein a dimension for each multi-dimensional bin corresponds to an attribute range of a respective one of the attributes in the subset of attributes, computing a first set of interim predictive output values for a first attribute in the subset of attributes, wherein the first set of interim predictive output values is generated from a first subset of the training data within a first range of the attribute ranges, computing a first set of smoothed interim output values by applying a smoothing function to the first set of interim predictive output values, computing a second set of interim predictive output values for a second attribute in the subset of attributes, wherein the second set of interim predictive output values is generated from a second subset of the training data within a second range of the attribute ranges, computing a second set of smoothed interim output values by applying the smoothing function to the second set of interim predictive output values, and outputting a dataset for the transformed attribute, the dataset having, at least, a first dimension including the first set of smoothed interim output values and a second dimension including the second set of smoothed interim output values; and train the machine-learning model with the transformed attribute. - View Dependent Claims (2, 3, 4, 5, 6, 7)
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8. A method comprising:
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accessing, from a non-transitory computer-readable medium, (i) a machine-learning model that transforms input attribute values into a predictive or analytical output value for an entity associated with the input attribute values and (ii) training data for training the machine-learning model, wherein the training data are grouped into attributes; selecting, by a processing device, a subset of attributes from the attributes of the training data; transforming, by the processing device, the subset of attributes into a transformed attribute by performing operations comprising; grouping (a) a first portion of the training data for the subset of attributes into a first multi-dimensional bin and (b) a second portion of the training data for the subset of attributes into a second multi-dimension bin wherein a dimension for each multi-dimensional bin corresponds to an attribute range of a respective on of the attributes in the subset of attributes, computing a first set of interim predictive output values for a first attribute in the subset of attributes, wherein the first set of interim predictive output values is generated from a first subset of the training data within a first range of the attribute ranges, computing a first set of smoothed interim output values by applying a smoothing function to the first set of interim predictive output values, computing a second set of interim predictive output values for a second attribute in the subset of attributes, wherein the second set of interim predictive output values is generated from a second subset of the training data within a second range of the attribute ranges, computing a second set of smoothed interim output values by applying the smoothing function to the second set of interim predictive output values, and outputting a dataset for the transformed attribute, the dataset having, at least, a first dimension including the first set of smoothed interim output values and a second dimension including the second set of smoothed interim output values; and training, by the processing device, the machine-learning model with the transformed attribute. - View Dependent Claims (9, 10, 11, 12, 13, 14)
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15. A non-transitory computer-readable medium in which instructions executable by a processing device are stored for causing the processing device to:
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access (i) a machine-learning model that is a memory structure comprising input nodes interconnected with one or more output nodes via intermediate nodes, wherein the intermediate nodes are configured to transform input attribute values into a predictive or analytical output value for an entity associated with the input attribute values and (ii) training data for training the machine-learning model, wherein the training data are grouped into attributes; select a subset of attributes from the attributes of the training data; transform the subset of attributes into a transformed attribute by performing operations comprising; grouping (a) a first portion of the training data for the subset of attributes into a first multi-dimensional bin and (b) a second portion of the training data for the subset of attributes into a second multi-dimension bin, wherein a dimension for each multi-dimensional bin corresponds to an attribute range of a respective one of the attributes in the subset of attributes, computing a first set of interim predictive output values for a first attribute in the subset of attributes, wherein the first set of interim predictive output values is generated from a first subset of the training data within a first range of the attribute ranges, computing a first set of smoothed interim output values by applying a smoothing function to the first set of interim predictive output values, computing a second set of interim predictive output values for a second attribute in the subset of attributes, wherein the second subset of interim predictive output values is generated from a second subset of the training data within a second range of the attribute ranges, computing a second set of smoothed interim output values by applying the smoothing function to the second set of interim predictive output values, and outputting a dataset for the transformed attribute, the dataset having, at least, a first dimension including the first set of smoothed interim output values and a second dimension including the second set of smoothed interim output values; and train the machine-learning model with the transformed attribute. - View Dependent Claims (16, 17, 18, 19, 20)
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Specification