Method and system for automated modeling

• US 20080279434A1
• Filed: 05/11/2007
• Published: 11/13/2008
• Est. Priority Date: 05/11/2007
• Status: Abandoned Application

• Current Assignee: William Cassill
• Original Assignee: William Cassill
• Inventors: Cassill, William
• Application Number:
  US11/803,156
• Publication Number:
  US 20080279434A1
• Time in Patent Office: Days
• US Class Current: 382/131
• CPC Class Codes: G06Q 10/06 Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling

Abstract

Embodiments of the present invention include automated methods and systems for statistical modeling in high-dimensional problem domains. The automated statistical-analysis methods and systems of the present invention employ computationally efficient methods for preparing large amounts of high-dimensional data for analysis, computationally efficient methods for selecting and transforming predictors, and, based on these methods, computationally efficient model-building methods to generate effective prediction models. Embodiments of the present invention are especially useful when the high-dimensional nature of a problem domain exceeds that of problem domains that can be analyzed by human statisticians, or by human-guided automated systems, within reasonable time and budget constraints.

Claims

1. An automated data-analysis system comprising:

a data-set and data-dictionary receiving routine;

an automated model-building-and-validation program that constructs a predictive model from a received data set and data dictionary; and

an automated means for applying the predictive model to subsequently received data to predict values for a dependent variable and store the predicted values in computer-readable form in a computer-readable medium.

2. The automated data-analysis system of claim 1 wherein the automated model-building-and-validation program further includes:

data-normalization logic that automatically supplies default values for missing data, replaces extreme data values, and transforms categorical data to numeric data;

initial-predictor-selection logic that selects a first, initial set of predictors from the data set;

predictor-transformation logic that adds linear-transformation-related predictors to the first, initial set of predictors to produce a final, initial set of predictors;

model-building logic that selects a final set of predictors from the initial set of predictors, the final set of predictors comprising a predictive model;

model-validation logic that validates the predictive model;

final-predictor profiling logic that generates profiles of the final predictors; and

script-generation logic that supplements the predictive model with scripts that automate data-value transformations needed by the predictive model.

3. The automated data-analysis system of claim 2 wherein the data set includes a number of rows, each row a record representing an entity described by the row, and each row including values for each of a number of columns that represent the fields within each record.

4. The automated data-analysis system of claim 2

wherein the columns comprise variables, including independent variables that constitute potential predictors, and the dependent variable;

and

wherein variables include continuous variables and categorical variables.

5. The automated data-analysis system of claim 4 wherein the data-normalization logic comprises one or more routines that:

collapse all categorical values of each categorical variable that occur with less than a threshold frequency in the data set to a single, catch-all categorical value;

assign the catch-all categorical value for a categorical variable to the categorical-variable value of rows missing a value for the categorical variable; and

transform categorical variables into continuous, numeric variables by replacing each categorical-variable value with average values of the dependent variable for all rows with dependent-variable value equal to the categorical-variable value.

6. The automated data-analysis system of claim 5 wherein the data-normalization logic further comprises one or more routines that:

set missing data values for continuous variables to “

O;

” and

replaces data values for continuous variables less than a minimum-threshold value to the minimum-threshold value and data values for continuous variables greater than a maximum-threshold value to the maximum-threshold value, respectively.

7. The automated data-analysis system of claim 6 wherein the initial-predictor-selection logic that selects a first, initial set of predictors from the data set by computing a correlation coefficient, for each potential predictor in the data set, that represents a degree to which the potential predictor is correlated with the dependent variable, and by then selecting, as the first, initial set of predictors, those potential predictors most strongly correlated with the dependent variable as determined by the value of the corresponding computed correlations for the potential predictors.

8. The automated data-analysis system of claim 7 wherein the correlation coefficient is an absolute value of a Pearson'"'"'s correlation coefficient, with potential predictors having highest absolute values of computed Pearson'"'"'s correlation coefficients selected as the first, initial set of predictors.

9. The automated data-analysis system of claim 5 wherein the predictor-transformation logic further comprises one or more routines that:

for each predictor in the first, initial set of predictors,

adds rescaled predictors for the predictor and transformed predictors to the temporary set of predictors;

selects linear transforms for the predictor and include transformed predictors, based on the selected linear transformations, along with the predictor in a temporary set of predictors;

selects additional predictors related to the predictor by forward, stepwise regression with respect to a portion of the data set; and

adds the predictor and additional predictors to the final, initial set of predictors.

10. The automated data-analysis system of claim 5 wherein the model-building logic further comprises one or more routines that:

selects an intermediate set of predictors by forward, stepwise regression with respect to a portion of the data set; and

refines the intermediate set of predictors by backwards elimination to produce a final set of predictors.

11. The automated data-analysis system of claim 5 wherein the model-building logic further comprises one or more routines that:

iteratively,

randomly selects a number of rows from the data set and potential predictors from the final, initial set of predictors to produce a small model;

selects, by forward regression with respect to a residual set of values, a next set of additional predictors;

adds the next set of additional predictors to the final set of predictors; and

regresses the final set of predictors with respect to a portion of the database to generate an updated, residual value for use in a subsequent iteration

until either convergence or execution of maximum number of iterations.

12. The automated data-analysis system of claim 5 wherein the model-validation logic further comprises one or more routines that:

generates a decile-divided set of predicted values for each of a first and second portion of the data set;

computes metrics for each decile of the decile-divided set of predicted values, including cumulative gain, lift, cumulative lift, average predicted dependent-variable value, and average dependent-variable value; and

compares the computed metrics to determine whether the two decile-divided sets are sufficiently closely related, and whether the deciles within the two decile-divided sets are sufficiently well differentiated from one another by computed metric values, to designate the predictive model valid.