AUTOMATED MACHINE LEARNING TOOL FOR EXPLAINING THE EFFECTS OF COMPLEX TEXT ON PREDICTIVE RESULTS
An apparatus comprising feature engineering and text explanation modules for explaining text from predictive results of an algorithmic model. The feature engineering module creates vectors for string variables, each string variable comprising identified text, each vector created comprising a numeric combination, each numeric combination identifying a variable name and a value having a word or a phrase. The feature engineering module causes a predictive engine to generate predictive results using the algorithmic model, the data set, and the vectors created. The predictive results comprising the string variable or a modified version of the string variable and a confidence score. The text explanation module maps words and phrases from qualified text of the string variable, or modified version, to the numeric combinations of the vectors and determines a probability score for each word and each phrase. The most influential words and phrases are plotted on a chart.
1 . An apparatus for explaining text from predictive results generated by at least one algorithmic model, the apparatus comprising:
a feature engineering module configured by a processor to:
create a plurality of vectors for at least one string variable, with each string variable comprising identified text, each vector created comprising a numeric combination, each numeric combination identifying a variable name and at least one selected from a group comprising a value having a word and another value having a phrase; and
cause a predictive engine to generate predictive results using the at least one algorithmic model, the data set, and the vectors created, the predictive results comprising the at least one string variable or a modified version of the at least one string variable and at least one confidence score associated with the at least one string variable or the modified version of the at least one string variable;
a text explanation module configured by the processor to:
map at least one selected from a group comprising words and at least one phrase from qualified text of the at least one string variable to the numeric combinations of the vectors;
determine a probability score for each word and each phrase; and
generate chart variables and plot variables, the plot variables comprising at least one of selected from a group comprising the most influential words and the most influential phrases, the most influential words and phrases based on the probability scores.
2 . The apparatus of claim 1 , further comprising a text detection module configured by a processor to:
determine the identified text, the identified text determined based on at least one selected from a group comprising a set of rules and a minimal confidence score, the identified text having at least one variable name associated with a variable of the data set and a variable value comprising at least one selected from a group comprising one or more sentences and one or more paragraphs; and
the one or more sentences and the one or more paragraphs comprising at least one selected from a group comprising a plurality of words and at least one phrase.
3 . The apparatus of claim 2 , wherein the set of rules is a-priori information, the set of rules determined based on a metric, the metric defining a minimal length of text and variability of at least one selected from a group comprising words and phrases, and variable names or variable metadata.
4 . The apparatus of claim 1 , wherein the feature engineering module is configured by the processor to determine a number of vectors for the identified text.
5 . The apparatus of claim 4 , wherein the number of vectors is a-priori information, the number of vectors for the identified text determined based on at least one text corpus and a functional form.
6 . The apparatus of claim 1 , wherein the text explanation module is configured by the processor determine qualified text based on the at least one confidence score.
7 . The apparatus of claim 1 , wherein the text explanation module is configured by the processor to determine the probability score using Bayes' theorem for each word and for each phrase.
8 . A system for explaining text from predictive results generated by at least one algorithmic model, the system comprising:
a feature engineering module configured by a processor to:
create a plurality of vectors for at least one string variable, with each string variable comprising identified text, each vector created comprising a numeric combination, each numeric combination identifying a variable name and at least one selected from a group comprising a value having a word and another value having a phrase;
a predictive engine module configured by the processor to:
generate at least one predictive result using the at least one algorithmic model, the data set, and the vectors created, the predictive results comprising the at least one string variable or a modified version of the at least one string variable and at least one confidence score associated with the at least one string variable or the modified version of the at least one string variable;
a text explanation module configured by the processor to:
map at least one selected from a group comprising words and at least one phrase from qualified text of the at least one string variable to the numeric combinations of the vectors;
determine a probability score for each word and each phrase; and
generate chart variables and plot variables, the plot variables comprising at least one of selected from a group comprising the most influential words and the most influential phrases, the most influential words and phrases based on the probability scores.
9 . The system of claim 8 , wherein the predictive engine generates the at least one predictive result based on an outcome variable using the at least one algorithmic model, the at least one predictive result comprising the at least one string variable and the at least one confidence score.
10 . The system of claim 8 , further comprising a text detection module configured by a processor to:
determine the identified text, the identified text determined based on at least one selected from a group comprising a set of rules and a minimal confidence score, the identified text having at least one variable name associated with a variable of the data set and a variable value comprising at least one selected from a group comprising one or more sentences and one or more paragraphs; and
the one or more sentences and the one or more paragraphs comprising at least one selected from a group comprising a plurality of words and at least one phrase.
11 . The system of claim 10 , wherein the set of rules is a-priori information, the set of rules determined based on a metric, the metric defining a minimal length of text and variability of at least one selected from a group comprising words and phrases, and variable names or variable metadata.
12 . The system of claim 8 , wherein the feature engineering module is configured by the processor to determine a number of vectors for the identified text.
13 . The system of claim 12 , wherein the number of vectors is a-priori information, the number of vectors for the identified text determined based on at least one text corpus and a functional form.
14 . The system of claim 8 , wherein the text explanation module is configured by the processor determine qualified text based on the at least one confidence score.
15 . The system of claim 8 , wherein the text explanation module is configured by the processor to determine the probability score using Bayes' theorem for each word and for each phrase.
16 . A method for explaining text from predictive results generated by at least one algorithmic model, the method comprising:
creating a plurality of vectors for at least one string variable, with each string variable comprising identified text, each vector created comprising a numeric combination, each numeric combination identifying a variable name and at least one selected from a group comprising a value having a word and another value having a phrase;
generating at least one predictive result using the at least one algorithmic model, the data set, and the vectors created, the predictive results comprising the at least one string variable or a modified version of the at least one string variable and at least one confidence score associated with the at least one string variable or the modified version of the at least one string variable;
mapping at least one selected from a group comprising words and at least one phrase from qualified text of the at least one string variable to the numeric combinations of the vectors;
determining a probability score for each word and each phrase; and
generating chart variables and plot variables, the plot variables comprising at least one of selected from a group comprising the most influential words and the most influential phrases, the most influential words and phrases based on the probability scores.
17 . The method of claim 16 , further comprising:
determining the identified text, the identified text determined based on at least one selected from a group comprising a set of rules and a minimal confidence score, the identified text having at least one variable name associated with a variable of the data set and a variable value comprising at least one selected from a group comprising one or more sentences and one or more paragraphs; and
the one or more sentences and the one or more paragraphs comprising at least one selected from a group comprising a plurality of words and at least one phrase.
18 . The method of claim 16 , further comprising determining a number of vectors for the identified text.
19 . The method of claim 16 , further comprising determining qualified text based on the at least one confidence score.
20 . The method of claim 16 , further comprising determining the probability score using Bayes' theorem for each word and for each phrase.