New Work Item for ISO Predictive Analytics (Initial Notes and Thoughts) Introduction


 Cameron Holland
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1 Introduction New Work Item for ISO Predictive Analytics (Initial Notes and Thoughts) Predictive analytics encompasses the body of statistical knowledge supporting the analysis of massive data sets. Massive data sets arise naturally via automated data collection associated with remote sensing, transactional (on line) purchases, web site browsing and viewing patterns, social media (networks and interactions), and so forth. Extracting useful information (leading to actionable items) from these data sets has spurred the statistical and other communities to address the problems (e.g., thousands of explanatory variables) and opportunities (sufficient data for validating models) inherent in Big Data applications. The historical core statistical methodologies (e.g., regression analysis) remain highly relevant although the usual emphasis on inference and hypothesis testing gives way to estimation and prediction. The nature of massive data sets has forced practitioners to assess the strengths and limitations of their methodologies and to extend where possible or to develop new techniques to take advantage of or to cope with the data size magnitudes. As statistical practitioners contend with the challenges of massive data sets, keeping current with the latest developments and methodologies in predictive analytics and data mining requires an understanding of the terminology. A further complication is that the advances are not attributable solely to the statistical community but are provided by computer scientists (machine learning), engineers (neural networks), and business intelligence professionals (customer relations management), among others. Consequently, some of the concepts in predictive analytics take on different names depending upon the originating field. For purposes of developing a coherent vocabulary, this international terminology standard will abide by the vocabulary structures previously developed in the ISO 3534 series and will expand them to include other terms pertinent to predictive analytics. In particular, regression analysis that facilitates estimation in the design of experiments context (ISO ) is perfectly suited as the basis for predictive modeling. Much of the methodology of predictive analytics can be reduced to relating response variables (continuous or discrete) to a set of explanatory variables or covariates. Massive data applications may not always be so neat in reducing to a set of response and explanatory variables, but may center upon determining relationships among a large set of variables. Identifying patterns and associations can have substantial business ramifications in spawning extra sales or upgraded choices. For example, in preparing to purchase a book on line, the provider may offer a list of books that have also been purchased by those buying the selected book. Similarly, in choosing a streaming video, the provider could indicate a set of movies that others enjoyed relative to a viewer s previous purchases. 1
2 Applications are driving the intense interest in predictive analytics and are likely to continue to do so as the opportunities are met with significant accomplishments. This terminology standard is intended to enhance the momentum and to deter duplication of efforts through an otherwise disparate vocabulary across disciplines. Plan of Attack In developing a terminology standard, one of the first steps is to collect the terms to be included (a tentative initial list) and to construct concept diagrams to illustrate the inter relationships among terms. The following is a list of terms with a preliminary organizational structure to be refined as the concept diagrams are developed. This is the initial set of terms that need to be configured into concept diagrams, which are under development. Relevant terms available in ISO regression curve 2.21 regression surface Relevant terms available in ISO operating characteristic curve Relevant terms available in ISO model response variable predictor variable residual error, error term residual pure random error, pure error misspecification error interaction curvature degrees of freedom response surface design design matrix method of least squares regression analysis analysis of variance Basic statistical terms not found in 3534 series but needed here Detailed terms from an ANOVA table associated with regression: SSE, SST, SSR, MSE, R 2, lack of fit 2
3 Other terms used in regression not found in 3534 series Forward selection Backward selection Stepwise regression Best subset selection Logit function Lasso Penalty function Terms involving types of analyses Supervised/unsupervised learning Correlation analysis Principal components Cluster analysis k means clustering hierarchical clustering Non linear regression Logistic regression; logistic discrimination Odds ratio, log odds Ridge regression Discriminant analysis Dimension reduction Regression Decision trees + CART Neural network analysis Market basket analysis, affinity analysis Customer relations management Terms involved in data preparation Summary statistics Missing data Missing at random; missing completely at random Outliers Hat matrix Cook s distance leverage Terms involving model selection AIC BIC Cp R 2 R 2  adjusted G 2, Gini Index Log worth 3
4 ROC curve (receiver operating characteristic curve) Sensitivity Specificity Lift Terms in non linear regression with their neural network analogues (following Kutner et al.) coefficient (weight) predictor (input) response (output) observation (exemplar) parameter estimation (training or learning) steepest descent (back propagation) intercept (bias term) derived predictor (hidden node) penalty function (weight decay) others not in Kutner s list: perceptron, nodes, activation functions, tanh, logit Terms involving decision trees Boosting Bagging Random forest Elastic net Prune Leaves Split Variable importance plot Other terms used in data mining, big data sets, etc. Support vector machines Vapnick dimension Singular value decomposition Extracts from Kutner et al. to help with the concept diagrams: 4
5 (Akaike Information criteria, Schwartz Bayesian criteria) 5
6 analytic disciplines compared 6
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