Incremental response modeling

A computing device comprising:

• a processor;
• and
• a non-transitory computer-readable medium operably coupled to the processor, the computer-readable medium having computer-readable instructions stored thereon that, when executed by the processor, cause the computing device to
• receive exposure group data generated from first responses by an exposure group, wherein the exposure group received a request to respond, wherein a response of the first responses is either positive or negative;
• receive control group data generated from second responses by a control group, wherein the control group did not receive the request to respond, wherein a response of the second responses is either positive or negative;
• identify the positive responses in the control group data;
• identify the positive responses in the exposure group data;
• (a) define a one-class support vector machine (SVM) model using the identified positive responses from the control group data and an upper bound parameter value;
• (b) execute the defined one-class SVM model with the identified positive responses from the exposure group data;
• (c) determine an error value based on execution of the defined one-class SVM;
• and
• (d) select a final one-class SVM model by validating the defined one-class SVM model using the determined error value.

A method of selecting a one-class support vector machine model for incremental response modeling, the method comprising:

• receiving exposure group data generated from first responses by an exposure group, wherein the exposure group received a request to respond, wherein a response of the first responses is either positive or negative;
• receiving control group data generated from second responses by a control group, wherein the control group did not receive the request to respond, wherein a response of the second responses is either positive or negative;
• identifying the positive responses in the control group data;
• identifying the positive responses in the exposure group data;
• (a) defining, by a computing device, a one-class support vector machine (SVM) model using the identified positive responses from the control group data and an upper bound parameter value;
• (b) executing, by the computing device, the defined one-class SVM model with the identified positive responses from the exposure group data;
• (c) determining, by the computing device, an error value based on execution of the defined one-class SVM model;
• and
• (d) selecting, by the computing device, a final one-class SVM model by validating the defined one-class SVM model using the determined error value.

A non-transitory computer-readable medium having stored thereon computer-readable instructions that when executed by a computing device cause the computing device to:

• receive exposure group data generated from first responses by an exposure group, wherein the exposure group received a request to respond, wherein a response of the first responses is either positive or negative;
• receive control group data generated from second responses by a control group, wherein the control group did not receive the request to respond, wherein a response of the second responses is either positive or negative;
• identify the positive responses in the control group data;
• identify the positive responses in the exposure group data;
• define a classification model using the identified positive responses from the control group data;
• execute the defined classification model with the identified positive responses from the exposure group data;
• determine an error value based on execution of the defined classification model;
• select a final classification model by validating the defined classification model using the determined error value;
• define a binary classification model using the exposure group data;
• execute the defined binary classification model with received data to predict positive responses and negative responses;
• execute the selected final classification model with the predicted positive responses of the received data to define outliers;
• and
• determine an incremental response as the defined outliers, wherein the incremental response comprises respondents that provide a positive response only when the request to respond is received.

A computing device comprising:

• a processor;
• and
• a non-transitory computer-readable medium operably coupled to the processor, the computer-readable medium having computer-readable instructions stored thereon that, when executed by the processor, cause the computing device to
• receive exposure group data generated from first responses by an exposure group, wherein the exposure group received a request to respond, wherein a response of the first responses is either positive or negative;
• receive control group data generated from second responses by a control group, wherein the control group did not receive the request to respond, wherein a response of the second responses is either positive or negative;
• identify the positive responses in the control group data;
• identify the positive responses in the exposure group data;
• define a classification model using the identified positive responses from the control group data;
• execute the defined classification model with the identified positive responses from the exposure group data;
• determine an error value based on execution of the defined classification model;
• select a final classification model by validating the defined classification model using the determined error value;
• define a binary classification model using the exposure group data;
• execute the defined binary classification model with received data to predict positive responses and negative responses;
• execute the selected final classification model with the predicted positive responses of the received data to define outliers;
• and
• determine an incremental response as the defined outliers, wherein the incremental response comprises respondents that provide a positive response only when the request to respond is received.

A method of identifying outliers in data for incremental response modeling, the method comprising:

• receiving exposure group data generated from first responses by an exposure group, wherein the exposure group received a request to respond, wherein a response of the first responses is either positive or negative;
• receiving control group data generated from second responses by a control group, wherein the control group did not receive the request to respond, wherein a response of the second responses is either positive or negative;
• identifying the positive responses in the control group data;
• identifying the positive responses in the exposure group data;
• defining, by a computing device, a classification model using the identified positive responses from the control group data;
• executing, by the computing device, the defined classification model with the identified positive responses from the exposure group data;
• determining, by the computing device, an error value based on execution of the defined classification model;
• selecting, by the computing device, a final classification model by validating the defined classification model using the determined error value;
• defining, by the computing device, a binary classification model using the exposure group data;
• executing, by the computing device, the defined binary classification model with received data to predict positive responses and negative responses;
• executing, by the computing device, the selected final classification model with the predicted positive responses of the received data to define outliers;
• and
• determining, by the computing device, an incremental response as the defined outliers, wherein the incremental response comprises respondents that provide a positive response only when the request to respond is received.