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The second edition of a bestseller, Statistical and Machine-Learning Data Mining: Techniques for Better Predictive Modeling and Analysis of Big Datais still the only book, to date, to distinguish between statistical data mining and machine-learning data mining. The first edition, titled Statistical Modeling and Analysis for Database Marketing: Effective Techniques for Mining Big Data,contained 17 chapters of innovative and practical statistical data mining techniques. In this second edition, renamed to reflect the increased coverage of machine-learning data mining techniques, the author has completely revised, reorganized, and repositioned the original chapters and produced 14 new chapters of creative and useful machine-learning data mining techniques. In sum, the 31 chapters of simple yet insightful quantitative techniques make this book unique in the field of data mining literature. The statistical data mining methods effectively consider big data for identifying structures (variables) with the appropriate predictive power in order to yield reliable and robust large-scale statistical models and analyses. In contrast, the author's own GenIQ Model provides machine-learning solutions to common and virtually unapproachable statistical problems. GenIQ makes this possible - its utilitarian data mining features start where statistical data mining stops. This book contains essays offering detailed background, discussion, and illustration of specific methods for solving the most commonly experienced problems in predictive modeling and analysis of big data. They address each methodology and assign its application to a specific type of problem. To better ground readers, the book provides an in-depth discussion of the basic methodologies of predictive modeling and analysis. While this type of overview has been attempted before, this approach offers a truly nitty-gritty, step-by-step method that both tyros and experts in the field can enjoy playing with.

The second edition of a bestseller, Statistical and Machine-Learning Data Mining: Techniques for Better Predictive Modeling and Analysis of Big Data is still the only book, to date, to distinguish between statistical data mining and machine-learning data mining. The first edition, titled Statistical Modeling and Analysis for Database Marketing: Effective Techniques for Mining Big Data, contained 17 chapters of innovative and practical statistical data mining techniques. In this second edition, renamed to reflect the increased coverage of machine-learning data mining techniques, the author has completely revised, reorganized, and repositioned the original chapters and produced 14 new chapters of creative and useful machine-learning data mining techniques. In sum, the 31 chapters of simple yet insightful quantitative techniques make this book unique in the field of data mining literature. The statistical data mining methods effectively consider big data for identifying structures (variables) with the appropriate predictive power in order to yield reliable and robust large-scale statistical models and analyses. In contrast, the author's own GenIQ Model provides machine-learning solutions to common and virtually unapproachable statistical problems. GenIQ makes this possible - its utilitarian data mining features start where statistical data mining stops. This book contains essays offering detailed background, discussion, and illustration of specific methods for solving the most commonly experienced problems in predictive modeling and analysis of big data. They address each methodology and assign its application to a specific type of problem. To better ground readers, the book provides an in-depth discussion of the basic methodologies of predictive modeling and analysis. While this type of overview has been attempted before, this approach offers a truly nitty-gritty, step-by-step method that both tyros and experts in the field can enjoy playing with.

The second edition of a bestseller, Statistical and Machine-Learning Data Mining: Techniques for Better Predictive Modeling and Analysis of Big Data is still the only book, to date, to distinguish between statistical data mining and machine-learning data mining. The first edition, titled Statistical Modeling and Analysis for Database Marketing: Effective Techniques for Mining Big Data, contained 17 chapters of innovative and practical statistical data mining techniques. In this second edition, renamed to reflect the increased coverage of machine-learning data mining techniques, the author has completely revised, reorganized, and repositioned the original chapters and produced 14 new chapters of creative and useful machine-learning data mining techniques. In sum, the 31 chapters of simple yet insightful quantitative techniques make this book unique in the field of data mining literature. The statistical data mining methods effectively consider big data for identifying structures (variables) with the appropriate predictive power in order to yield reliable and robust large-scale statistical models and analyses. In contrast, the author's own GenIQ Model provides machine-learning solutions to common and virtually unapproachable statistical problems. GenIQ makes this possible - its utilitarian data mining features start where statistical data mining stops. This book contains essays offering detailed background, discussion, and illustration of specific methods for solving the most commonly experienced problems in predictive modeling and analysis of big data. They address each methodology and assign its application to a specific type of problem. To better ground readers, the book provides an in-depth discussion of the basic methodologies of predictive modeling and analysis. While this type of overview has been attempted before, this approach offers a

From simple commercial relations to complex online transactions at this stage, it not only highlights the progress of science and technology, but also indirectly explains the diversified evolution of marketing methods and means. In marketing, database marketing has been favored by more marketers with its low cost and high efficiency and has become the “rookie” in marketing in recent years. However, as a kind of prediction and ferry, database marketing tends to be applied after simple data analysis in unpredictable market and in practice. In contrast, database marketing combined with machine learning algorithms has always been a depression in the marketing field. Therefore, this paper takes e-commerce as the research object and carries out database marketing research based on machine learning algorithm from four stages: theoretical preparation, status analysis, model construction, and results application. Firstly, the connotation, advantages, and specific operation procedures of database marketing are discussed. At the same time, four excellent machine learning algorithms including logistic regression, random forest, support vector machine, and gradient boosted decision tree (GBDT) are selected to explain the basic principles and algorithm introduction, respectively, laying a theoretical foundation for the model training chapter. Secondly, it analyzes the current situation of e-commerce from the distribution of marketing objects, the proportion of marketing channels, and the composition of marketing methods and finds new marketing ideas based on the main problems existing at the present stage of database marketing using machine learning algorithm. Thirdly, on the premise of marketing ideas, data acquisition, data processing, and positive and negative sample setting. At the same time, four machine learning algorithms are used to combine features from the perspectives of consumers, stores, and the relationship between consumers and stores. Finally, by substituting the predicted sample into the model for testing, the crowd whose predicted score is between 80 and 99 is selected to be put into the market as the model predicted crowd, and it is proposed that e-commerce should mainly adopt the database marketing method of model prediction. On the one hand, machine learning algorithm can solve the problem of uneven distribution of marketing objects, and on the other hand, it can effectively prevent the loss of potential consumers. In addition, the application strategy of optimizing other database marketing methods and assisting model prediction to improve marketing effect is also put forward.

Data sharing to the multiple organizations are essential for analysis in many situations. The shared data contains the individual’s private and sensitive information and results in privacy breach. To overcome the privacy challenges, privacy preserving data mining (PPDM) has progressed as a solution. This work addresses the problem of PPDM by proposing statistical transformation with intuitionistic fuzzy (STIF) algorithm for data perturbation. The STIF algorithm contains statistical methods weight of evidence, information value and intuitionistic fuzzy Gaussian membership function. The STIF algorithm is applied on three benchmark datasets adult income, bank marketing and lung cancer. The classifier models decision tree, random forest, extreme gradient boost and support vector machines are used for accuracy and performance analysis. The results show that the STIF algorithm achieves 99% of accuracy for adult income dataset and 100% accuracy for both bank marketing and lung cancer datasets. Further, the results highlights that the STIF algorithm outperforms in data perturbation capacity and privacy preserving capacity than the state-of-art algorithms without any information loss on both numerical and categorical data.

Real‐world data play a key role in monitoring drug safety at the post‐marketing stage. However, challenges on how to rapidly and continuously obtain analytical results of many outcomes for drug safety signal monitoring still remain. We aimed to establish a rapid and continuous monitoring tool for drug safety assessment based on real‐world data in Japan. An automated process for a new‐user cohort design with customizable analytical conditions was developed. The customizable analytical conditions include exposure and control drugs, 46 outcomes related to liver and kidney functions, blood tests, biomarkers, and time period of interest. Statistical analyses were performed to evaluate the outcome status (present/absent) and calculate the adjusted hazard ratio, with a 95% confidence interval of exposure to control. We monitored the safety signals of an anti‐COVID‐19 drug (combination of tixagevimab and cilgavimab) and compared them with those of two controls (peramivir and the combination of casirivimab and imdevimab) to examine the practical utility of this new tool. Our study provided helpful information (e.g., new safety signals) on many outcomes at multiple time points, which could enhance the understanding of drug safety profiles soon after approval. Our function can be used to rapidly and continuously monitor drug safety signals and contribute to strengthening drug safety monitoring in Japan.

Imbalanced datasets pose a persistent challenge in business data mining, particularly in high-stakes domains such as financial risk prediction and customer churn analysis, where the minority class often carries disproportionate operational and financial consequences. Although widely used evaluation metrics–such as accuracy, precision, recall, F1-score, and Matthews Correlation Coefficient (MCC)–are commonly applied in practice, there remains no empirical consensus on which metric offers the most reliable performance under real-world conditions. Existing studies lack a unified, statistically validated framework that accounts for threshold sensitivity, input noise, and interpretability–factors critical to business decision-making. To address this gap, we present a comprehensive and statistically rigorous evaluation of performance metrics for imbalanced business classification tasks. Using two benchmark datasets with distinct sizes and imbalance ratios–the Default of Credit Card Clients dataset and the Telco Customer Churn dataset–we evaluate five commonly used machine learning models: Logistic Regression (LR), Decision Tree (DT), Random Forest (RF), Extreme Gradient Boosting (XGBoost), and k-Nearest Neighbors (KNN). Our methodology incorporates static and dynamic threshold analysis, Gaussian noise robustness testing, bootstrap confidence intervals, McNemar’s test, Cohen’s kappa, and analysis of variance (ANOVA) to assess the statistical reliability of performance metrics. In addition, we introduce a novel two-stage explainable artificial intelligence (XAI) framework using SHapley Additive exPlanations (SHAP). The first stage employs standard SHAP visualizations (bar and beeswarm plots) to ensure baseline interpretability. The second stage extends this with a novel 3D metric-conditioned SHAP analysis, linking feature contributions to variations in classification thresholds and evaluation metrics. Our findings show that the F1-score consistently provides the most stable and balanced evaluation across datasets and testing conditions, with MCC offering complementary diagnostic value. In contrast, accuracy and precision demonstrate limited robustness under class imbalance. By combining statistical rigor with interpretable AI, this study offers the most comprehensive guidance to date for selecting performance metrics in imbalanced business classification, with practical implications for model deployment in finance, marketing, and customer analytics.

The thesis presented here is that biomedical research is based on the trusted exchange of services. That exchange would be conducted more efficiently if the trusted software platforms to exchange those services, if they exist, were more integrated. While simpler and narrower in scope than the services governing biomedical research, comparison to existing internet-based platforms, like Airbnb, can be informative. We illustrate how the analogy to internet-based platforms works and does not work and introduce The Commons, under active development at the National Institutes of Health (NIH) and elsewhere, as an example of the move towards platforms for research.

Most library science surveys use Likert-style questions. After you read a study that reports results from Likert-style questions. After you read a study that reports results from Likert-style questions, you may think about using the results to guide some decision that you need to make. If so, then your job is to understand the validity of the results and how they apply to your decision. The researcher presents the results. You must determine how persuasive the results are for you. You have probably filled out a questionnaire that asks you to choose one response from several that are offered. Keep in mind the possibility of confirmation bias. They are all human. Many researchers may have a subliminal desire to prove their hypotheses are correct. Parametric analyses are more powerful; in other words, they can find a statistical difference that is not identified by nonparametric analyses.