Predictive Data Mining in Very Large Data Sets: A Demonstration and Comparison Under Model Ensemble

Dr. Hongwei “Patrick” Yang

Educational Policy Studies & Evaluation

College of Education

University of Kentucky

Lexington, KY

Presented at the 2014 Modern Modeling Methods conference

Overview

The study demonstrates predictive data mining models under model ensemble in the context of analyzing large data

Data mining is usually defined as the data-driven process of discovering meaningful hidden patterns in large amounts of data through automatic as well as manual means

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Overview

Many industries use data mining to address business problems, such as bankrupt prediction, risk management, fraud detection, etc.

Such applications in data mining typically take advantage of predictive data mining models as learning machines with a primary focus on making good predictions

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Overview

Among many types of predictive data mining models are decision trees, neural networks, and (traditional) regression models:

Decision tree: Identify the most significant split of the outcome at each layer

Neural network: Model nonlinear associations

For each of the models/learning machines presented above, the outcome can be either a categorical one or a numerical one

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Overview

On the other hand, model ensemble techniques have recently become popular thanks to the growing power of computation

Bagging and boosting are two of the most popular ensemble techniques

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Overview

Model ensemble techniques are designed to create a model ensemble/committee containing multiple component/base models

The committee of models are averaged or pooled in a certain manner to improve the stability and accuracy of predictions

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Overview

Model ensemble techniques can be incorporated into many types of predictive models/learning machines (tree, neural network, regression, etc.)

Ensemble-based modeling can also be combined with common feature/subset selection procedures (genetic algorithm, stepwise method, all-possible-subsets, etc.)

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Numerical examples

To demonstrate the effectiveness of predictive data mining in discovering meaningful information from large data, the study chooses the three types of predictive models which are commonly used, and analyzes them under two large scale applications

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Numerical examples

To further improve the predictions from each type of model, model ensemble is implemented during the modeling process to pool predictions from individual component model

For comparison purposes, all models are also fitted without creating any model ensemble

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Numerical examples

Besides, the models are each evaluated for goodness-of-fit and performance at the final stage using various fit statistics including average squared error, ROC index, misclassification rate, Gini coefficient, K-S statistic, as applicable

The entire analysis is performed under SAS Enterprise Miner 7.1

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Numerical examples

Example one: Physicochemical properties of protein tertiary structure data

A numerical outcome: 45,730 cases

Example two: Bank marketing data

A categorical outcome: 41,188 cases

Both data sets are retrieved from the UC Irvine (UCI) Machine Learning Repository

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Example one: Numerical outcome

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Example one: Numerical outcome

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Table 1. Comparison of Models based on Training Data under a Numerical Outcome.

Model Description

Average Squared

Error

Root Average

Squared Error

Maximum

Absolute Error

EnRegTreeNN 21.338 4.619 15.000

EnReg 22.874 4.783 14.818

EnNN 23.122 4.809 16.556

EnTree 25.193 5.019 16.131

NN 23.591 4.857 19.663

Reg 23.574 4.855 19.668

Tree 24.103 4.910 17.412

Example one: Numerical outcome

Ensemble models tend to be more effective in reducing errors, although it is not guaranteed

Average squared error: Lower is better

Root average squared error: Lower is better

Maximum absolute error: Lower is better

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Example two: Categorical outcome

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Example two: Categorical outcome

Table 2. Comparison of Models based on Training Data under a Categorical Outcome.

Model

Description

Root

Average

Squared

Error

Misclassification

Rate

Roc

Index

Gini

Coefficient

Kolmogorov

-Smirnov

Statistic

Bin-Based

Two-Way

Kolmogorov

-Smirnov

Statistic

Gain Cumulative

Lift

Cumulative

Percent

Captured

Response

EnRegTreeNN 0.237 0.078 0.947 0.894 0.780 0.772 504.305 6.043 60.541

EnReg 0.241 0.081 0.935 0.871 0.719 0.717 455.744 5.557 55.676

EnNN 0.252 0.086 0.919 0.838 0.682 0.681 428.767 5.288 52.973

EnTree 0.270 0.101 0.801 0.602 0.579 0.576 395.325 4.953 49.623

Tree 0.254 0.090 0.900 0.800 0.697 0.692 441.595 5.416 54.179

NN 0.261 0.098 0.912 0.823 0.675 0.670 400.087 5.001 50.027

Reg 0.261 0.097 0.912 0.823 0.668 0.666 408.710 5.087 50.889

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Example two: Categorical outcome

Ensemble models typically have better discriminatory power among all models, as is indicated by each criterion Misclassification rate: Lower is better

ROC index: Higher is better

Gini coefficient: Higher is better

K-S statistic: Higher is better

Cumulative lift: Higher is better

Cumulative percent captured response: Higher is better

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Conclusions

The study presents some initial evidence for the effectiveness of model ensemble in improving the performance of an individual learning machine (model) under a given type

The study needs to be supplemented with additional information on the use of (real) bagging and boosting in improving the performance of individual learning machine

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Conclusions

The study provides applied researchers with more options beyond traditional regression modeling when reliable predictions are needed in their research

The study serves as the foundation for a future research topic which adds feature selection to predictive data mining modeling under model ensemble for analyzing very large data sets

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