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The mixture cure model is widely used to analyze survival data in the presence of a cured subgroup. Standard logistic regression‐based approaches to model the incidence may lead to poor predictive accuracy of cure, specifically when the covariate effect is non‐linear. Supervised machine learning techniques can be used as a better classifier than the logistic regression due to their ability to capture non‐linear patterns in the data. However, the problem of interpret‐ability hangs in the balance due to the trade‐off between interpret‐ability and predictive accuracy. We propose a new mixture cure model where the incidence part is modeled using a decision tree‐based classifier and the proportional hazards structure for the latency part is preserved. The proposed model is very easy to interpret, closely mimics the human decision‐making process, and provides flexibility to gauge both linear and non‐linear covariate effects. For the estimation of model parameters, we develop an expectation maximization algorithm. A detailed simulation study shows that the proposed model outperforms the logistic regression‐based and spline regression‐based mixture cure models, both in terms of model fitting and evaluating predictive accuracy. An illustrative example with data from a leukemia study is presented to further support our conclusion. [ABSTRACT FROM AUTHOR]

Statistical methods generating individualized treatment rules (ITRs) often focus on maximizing expected benefit, but these rules may expose patients to excess risk. For instance, aggressive treatment of type 2 diabetes (T2D) with insulin therapies may result in an ITR which controls blood glucose levels but increases rates of hypoglycemia, diminishing the appeal of the ITR. This work proposes two methods to identify risk‐controlled ITRs (rcITR), a class of ITR which maximizes a benefit while controlling risk at a prespecified threshold. A novel penalized recursive partitioning algorithm is developed which optimizes an unconstrained, penalized value function. The final rule is a risk‐controlled decision tree (rcDT) that is easily interpretable. A natural extension of the rcDT model, risk controlled random forests (rcRF), is also proposed. Simulation studies demonstrate the robustness of rcRF modeling. Three variable importance measures are proposed to further guide clinical decision‐making. Both rcDT and rcRF procedures can be applied to data from randomized controlled trials or observational studies. An extensive simulation study interrogates the performance of the proposed methods. A data analysis of the DURABLE diabetes trial in which two therapeutics were compared is additionally presented. An R package implements the proposed methods (https://github.com/kdoub5ha/rcITR). [ABSTRACT FROM AUTHOR]

Introduction: Clinical practice guidelines (hereafter 'guidelines') are crucial in providing evidence‐based recommendations for physicians and multidisciplinary teams to make informed decisions regarding diagnostics and treatment in various diseases, including cancer. While guideline implementation has been shown to reduce (unwanted) variability and improve outcome of care, monitoring of adherence to guidelines remains challenging. Real‐world data collected from cancer registries can provide a continuous source for monitoring adherence levels. In this work, we describe a novel structured approach to guideline evaluation using real‐world data that enables continuous monitoring. This method was applied to endometrial cancer patients in the Netherlands and implemented through a prototype web‐based dashboard that enables interactive usage and supports various analyses. Method: The guideline under study was parsed into clinical decision trees (CDTs) and an information standard was drawn up. A dataset from the Netherlands Cancer Registry (NCR) was used and data items from both instruments were mapped. By comparing guideline recommendations with real‐world data an adherence classification was determined. The developed prototype can be used to identify and prioritize potential topics for guideline updates. Results: CDTs revealed 68 data items for recording in an information standard. Thirty‐two data items from the NCR were mapped onto information standard data items. Four CDTs could sufficiently be populated with NCR data. Conclusion: The developed methodology can evaluate a guideline to identify potential improvements in recommendations and the success of the implementation strategy. In addition, it is able to identify patient and disease characteristics that influence decision‐making in clinical practice. The method supports a cyclical process of developing, implementing and evaluating guidelines and can be scaled to other diseases and settings. It contributes to a learning healthcare cycle that integrates real‐world data with external knowledge. [ABSTRACT FROM AUTHOR]

This paper reports on progress in the development of a teaching module on machine learning with decision trees for secondary‐school students, in which students use survey data about media use to predict who plays online games frequently. This context is familiar to students and provides a link between school and everyday experience. In this module, they use CODAP's "Arbor" plug‐in to manually build decision trees and understand how to systematically build trees based on data. Further on, the students use a menu‐based environment in a Jupyter Notebook to apply an algorithm that automatically generates decision trees and to evaluate and optimize the performance of these. Students acquire technical and conceptual skills but also reflect on personal and social aspects of the uses of algorithms from machine learning. [ABSTRACT FROM AUTHOR]

This paper reports on progress in the development of a teaching module on machine learning with decision trees for secondary‐school students, in which students use survey data about media use to predict who plays online games frequently. This context is familiar to students and provides a link between school and everyday experience. In this module, they use CODAP's "Arbor" plug‐in to manually build decision trees and understand how to systematically build trees based on data. Further on, the students use a menu‐based environment in a Jupyter Notebook to apply an algorithm that automatically generates decision trees and to evaluate and optimize the performance of these. Students acquire technical and conceptual skills but also reflect on personal and social aspects of the uses of algorithms from machine learning. [ABSTRACT FROM AUTHOR]

Taxonomic identification of fossils based on morphometric data traditionally relies on the use of standard linear models to classify such data. Machine learning and decision trees offer powerful alternative approaches to this problem but are not widely used in palaeontology. Here, we apply these techniques to published morphometric data of isolated theropod teeth in order to explore their utility in tackling taxonomic problems. We chose two published datasets consisting of 886 teeth from 14 taxa and 3020 teeth from 17 taxa, respectively, each with five morphometric variables per tooth. We also explored the effects that missing data have on the final classification accuracy. Our results suggest that machine learning and decision trees yield superior classification results over a wide range of data permutations, with decision trees achieving accuracies of 96% in classifying test data in some cases. Missing data or attempts to generate synthetic data to overcome missing data seriously degrade all classifiers predictive accuracy. The results of our analyses also indicate that using ensemble classifiers combining different classification techniques and the examination of posterior probabilities is a useful aid in checking final class assignments. The application of such techniques to isolated theropod teeth demonstrate that simple morphometric data can be used to yield statistically robust taxonomic classifications and that lower classification accuracy is more likely to reflect preservational limitations of the data or poor application of the methods. [ABSTRACT FROM AUTHOR]

This paper reports on progress in the development of a teaching module on machine learning with decision trees for secondary-school students, in which students use survey data about media use to predict who plays online games frequently. This context is familiar to students and provides a link between school and everyday experience. In this module, they use CODAP's "Arbor" plug-in to manually build decision trees and understand how to systematically build trees based on data. Further on, the students use a menu-based environment in a Jupyter Notebook to apply an algorithm that automatically generates decision trees and to evaluate and optimize the performance of these. Students acquire technical and conceptual skills but also reflect on personal and social aspects of the uses of algorithms from machine learning. [ABSTRACT FROM AUTHOR]

Background: As the population ages, mild cognitive impairment (MCI) and type 2 diabetes mellitus (T2DM) become common conditions that often coexist. Evidence has shown that MCI could lead to reduced treatment compliance, medication management, and self‐care ability in T2DM patients. Therefore, early identification of those with increased risk of MCI is crucial from a preventive perspective. Given the growing utilization of decision trees in prediction of health‐related outcomes, this study aimed to identify MCI in T2DM patients using the decision tree approach. Methods: This hospital‐based case–control study was performed in the Endocrinology Department of Xiangya Hospital affiliated to Central South University between March 2021 and December 2022. MCI was defined based on the Petersen criteria. Demographic characteristics, lifestyle factors, and T2DM‐related information were collected. The study sample was randomly divided into the training and validation sets in a 7:3 ratio. Univariate and multivariate analyses were performed, and a decision tree model was established using the chi‐square automatic interaction detection (CHAID) algorithm to identify key predictor variables associated with MCI. The area under the curve (AUC) value was used to evaluate the performance of the established decision tree model, and the performance of multivariate regression model was also evaluated for comparison. Results: A total of 1001 participants (705 in the training set and 296 in the validation set) were included in this study. The mean age of participants in the training and validation sets was 60.2 ± 10.3 and 60.4 ± 9.5 years, respectively. There were no significant differences in the characteristics between the training and validation sets (p >.05). The CHAID decision tree analysis identified six key predictor variables associated with MCI, including age, educational level, household income, regular physical activity, diabetic nephropathy, and diabetic retinopathy. The established decision tree model had 15 nodes composed of 4 layers, and age is the most significant predictor variable. It performed well (AUC =.75 [95% confidence interval (CI):.71–.78] and.67 [95% CI:.61–.74] in the training and validation sets, respectively), was internally validated, and had comparable predictive value compared to the multivariate logistic regression model (AUC =.76 [95% CI:.72–.80] and.69 [95% CI:.62–.75] in the training and validation sets, respectively). Conclusion: The established decision tree model based on age, educational level, household income, regular physical activity, diabetic nephropathy, and diabetic retinopathy performed well with comparable predictive value compared to the multivariate logistic regression model and was internally validated. Due to its superior classification accuracy and simple presentation as well as interpretation of collected data, the decision tree model is more recommended for the prediction of MCI in T2DM patients in clinical practice. [ABSTRACT FROM AUTHOR]

A variety of novel behavioral modeling techniques have been reported to accurately capture the nonlinear characteristics of GaN devices. For the purpose of describing GaN HEMT large‐signal behavior, machine learning‐based approaches have been proposed. There is, however, a lack of comparison and analysis of their performance with different machine learning techniques in these studies. An examination of machine learning‐based large‐signal modeling techniques is presented in this article. To develop large‐signal models of GaN HEMT, a range of commonly used modeling techniques such as artificial neural networks (ANN), random sample consistency (RANSAC), support vector regression (SVR), Gaussian process regression (GPR), decision trees (DTs), and genetic algorithm‐assisted ANN (GA‐ANN) are described and employed. Afterwards, integrated modeling techniques such as Bootstrap aggregation (BA), random forests (RF), AdaBoost, and gradient tree boosting (GTB) are reviewed and tested for their capabilities in developing GaN HEMT LSM. The hyperparameters of each built model are modified using the random search optimization (RSO) method, and five‐fold cross‐validation is used during validation. In order to identify optimal algorithms for nonlinear behavioral modeling of GaN devices, the model prediction results are comprehensively analyzed. [ABSTRACT FROM AUTHOR]

Summary: University students in other disciplines without prior knowledge in statistics and/or programming language are introduced to the statistical method of decision trees in the programming language R during a 45‐minute teaching and practice session. Statistics and programming skills are now frequently required within a wide variety of research fields and private industries. However, students unfamiliar with these subjects may be reluctant to join a full course because of time or student workloads or other commitments or a belief it is not for them. The proposed session is short and can be used as an ice‐breaker to let students have a basic understanding of running statistical models in programming language. [ABSTRACT FROM AUTHOR]

Decision trees (DTs) are a widely used prediction tool, owing to their interpretability. Standard learning methods follow a locally optimal approach that trades off prediction performance for computational efficiency. Such methods can however be far from optimal, and it may pay off to spend more computational resources to increase performance. Monte Carlo tree search (MCTS) is an approach to approximate optimal choices in exponentially large search spaces. We propose a DT learning approach based on the Upper Confidence Bound applied to tree (UCT) algorithm, including procedures to expand and explore the space of DTs. To mitigate the computational cost of our method, we employ search pruning strategies that discard some branches of the search tree. The experiments show that proposed approach outperformed the C4.5 algorithm in 20 out of 31 datasets, with statistically significant improvements in the trade‐off between prediction performance and DT complexity. The approach improved locally optimal search for datasets with more than 1,000 instances, or for smaller datasets likely arising from complex distributions. This article is categorized under:Algorithmic Development > Hierarchies and TreesApplication Areas > Data Mining Software ToolsFundamental Concepts of Data and Knowledge > Data Concepts [ABSTRACT FROM AUTHOR]

Motivation: Industrial policy is on the government agenda in many countries at different stages of development. Despite an extensive academic literature, much of the discussion is too general to guide policy‐makers. Purpose: The article reviews the taxonomies of industrial policy in the literature, highlights some of their limitations and offers a simple alternative based on constraints to industrial development. It applies this framework in the context of one low‐income country —Ethiopia—and suggests a problem‐tree approach to address the binding constraints to growth in particular sectors. Methods: Case studies of Ethiopia's garment, leather goods and cut‐flowers sectors are used as illustrations of the simple taxonomy proposed. Conclusion: In low‐income countries, the key issues for industrial policy often relate to basic issues, such as the provision of adequate infrastructure and access to land, rather than to the choice of particular forms of incentive. Policy Implications: By focusing on different markets, the simple taxonomy proposed could clarify government options in deciding how best to overcome critical constraints to competitiveness in a given sector. [ABSTRACT FROM AUTHOR]

In this report are used two data sets involving the main antidiabetic enzyme targets α‐amylase and α‐glucosidase. The prediction of α‐amylase and α‐glucosidase inhibitory activity as antidiabetic is carried out using LDA and classification trees (CT). A large data set of 640 compounds for α‐amylase and 1546 compounds in the case of α‐glucosidase are selected to develop the tree model. In the case of CT‐J48 have the better classification model performances for both targets with values above 80%–90% for the training and prediction sets, correspondingly. The best model shows an accuracy higher than 95% for training set; the model was also validated using 10‐fold cross‐validation procedure and through a test set achieving accuracy values of 85.32% and 86.80%, correspondingly. Additionally, the obtained model is compared with other approaches previously published in the international literature showing better results. Finally, we can say that the present results provided a double‐target approach for increasing the estimation of antidiabetic chemicals identification aimed by double‐way workflow in virtual screening pipelines. [ABSTRACT FROM AUTHOR]

In seismic data acquisition, because of several factors, such as surface barriers, receiver failure, noise contamination and budget control, seismic records often exhibit irregular sampling in the space domain. As corrupted seismic records have a negative effect on seismic migration, inversion and interpretation, seismic trace interpolation is a key step in seismic data pre‐processing. In this paper, we propose a high‐efficiency and high‐precision seismic trace interpolation method for irregularly spatially sampled data by combining an extreme gradient boosting decision tree and principal component analysis in a semi‐supervised learning method. The adjacent trace number, sampling number and amplitudes of the effective seismic data were taken as features to build the training data set for the extreme gradient boosting decision tree. Principal component analysis is applied to remove redundant information and accelerate the training speed. This is different from the traditional trace interpolation method in that the proposed method is data‐driven; therefore, it does not require any assumptions. Compared with other deep learning‐based trace interpolation methods, the proposed method has fewer control parameters and learning labels and a smaller training cost. Experiments using synthetic and field data demonstrated the validity and flexibility of this trace interpolation method. [ABSTRACT FROM AUTHOR]

Despite today's developing healthcare technology, conventional colonoscopy is still a gold‐standard method to detect colon abnormalities. Due to the folded structure of the intestine and visual disturbances caused by artifacts, it can be hard for specialists to detect abnormalities during the procedure. Frames that include artifacts such as specular reflection, improper contrast levels from insufficient or excessive illumination gastric juice, bubbles, or residuals should be detected to increase an accurate diagnosis rate. In this work, both conventional machine learning and transfer learning methods have been used to detect non‐informative frames in colonoscopy videos. The conventional machine learning part consists of 5 different types of texture features, which are gray level co‐occurrence matrix (GLCM), gray level run length matrix (GLRLM), neighborhood gray‐tone difference matrix (NGTDM), focus measure operators (FMOs), and first‐order statistics. In addition to these methods, we utilized 8 different transfer learning models: AlexNet, SqueezeNet, GoogleNet, ShuffleNet, ResNet50, ResNet18, NasNetMobile, and MobileNet. The results showed that FMOs and decision tree combination gave the best accuracy and f‐measure values with almost 89% and 0.79%, respectively, for the conventional machine learning part. When the transfer learning part is taken into account, AlexNet (99.85%) and SqueezeNet (98.80%) have the highest performance metric results. This study shows the potential of both transfer learning and conventional machine learning algorithms to provide fast and accurate non‐informative frame detection to be used during a colonoscopy, which may be considered the initial step in identifying and classifying colon‐related diseases automatically to help guide physicians. [ABSTRACT FROM AUTHOR]

Objectives: To describe a new classification for intraoral scans based on the scan extension and to introduce a decision guideline to choose the scan extension for fabricating tooth‐ and implant‐supported fixed dental prostheses (FDPs). Overview: Multiple operator‐ and patient‐related factors have been identified that can decrease the scanning accuracy of intraoral scanners (IOSs), including scan extension. However, the decision criteria for selecting scan extension for fabricating tooth‐ and implant‐supported restorations is unclear. Based on the extension of the intraoral digital scans, three types of scans can be defined: half‐arch (anterior or posterior), extended half‐arch, and complete‐arch scan. Variables to consider when choosing the scan extension include the number and location of units being restored, as well as the extension and location of edentulous areas. Additionally, the accuracy of the virtual definitive cast and the accuracy of the maxillomandibular relationship captured by using IOSs should be differentiated. Conclusions: A decision tree for selecting the scan extension is presented. The decision is based on the number and location of units being restored, and the extension and location of edentulous areas. Intraoral scans with reduced scan extension are indicated when fabricating tooth‐ and implant‐supported crowns or short‐span fixed prostheses, when the patient does not have more than one missing tooth in the area of the dental arch included in the scan. For the remaining clinical conditions, complete‐arch intraoral scans are recommended. Clinical Significance: Scan extension is a clinician's decision that should be based on the number and location of units being restored and the extension and location of edentulous areas. Intraoral scans with a reduced scan extension is recommended, when possible. [ABSTRACT FROM AUTHOR]

The efficient solution of the AC optimal power flow (OPF) for all cases and electrical grids is not guaranteed. Heuristics, approximations and relaxations have been proposed aplenty, each with pros and cons. This work proposes to solve the AC OPF with binary decision trees (BDTs). The solution starts with the full feasible set of an AC OPF instance, and, iteratively, BDTs tighten the constraints/bounds of that set, to improve the occurring feasible set by the median of the objective function of the preceding set. The medians of the objective function of the progressively tightened feasible sets will converge to the global optimum of the AC OPF. Recent proofs for the estimate performance of top‐down BDT learning heuristics ensure convergence of the method to the global optimum, provided the feasible set is adequately sampled for the BDT training. The recursive implementation of the method and the inductive nature of BDTs may also yield dispatches at multiple optimality levels for the same AC OPF instance, to account for stochastic resources. The performance of the proposed AC OPF solution is assessed over multiple cases by NICTA and the IEEE PES task force on benchmarks for validation of emerging power system algorithms. [ABSTRACT FROM AUTHOR]

The partial label ranking problem is a general interpretation of the preference learning scenario known as the label ranking problem, the goal of which is to learn preference classifiers able to predict a complete ranking with ties over the finite set of labels of the class variable. In this paper, we use unsupervised discretization techniques (equal‐frequency and equal‐width binning) to heuristically select the threshold for the numerical features in the algorithms based on induction of decision trees (partial label ranking trees algorithm). Moreover, we adapt the most well‐known averaging (bootstrap aggregating and random forests) and boosting (adaptive boosting) ensemble methods to the partial label ranking problem, in order to improve the robustness of the built classifiers. We compare the proposed methods with the nearest neighbors‐based algorithm (instance based partial label ranking) over the standard benchmark datasets, showing that our versions of the ensemble methods are superior in terms of accuracy. Furthermore, they are affordable in terms of computational efficiency. [ABSTRACT FROM AUTHOR]

Breast cancer is possibly the deadliest illness in the world and the risks are gradually increasing. One out of eight women has the chance to be detected with breast cancer in their lifetime. The utmost cause for the higher fatality rates is the prolonged prognosis for the detection of breast cancer. The focus of this study is therefore to develop a better fuzzy expert system for the detection of breast cancer using decision tree analysis for deriving the rule base. For this classification problem, the input features of the dataset are converted into human‐understandable terms‐linguistic variables. The Mamdani Fuzzy Rule‐Based system is deployed as the main inference engine and the centroid method for the defuzzification process to convert the final fuzzy score into class labels‐ benign (not cancerous) or malignant (cancerous). A decision tree algorithm is applied the creating a novel set of 27 fuzzy rules which are fed into FRBS. The investigation is performed on the publicly available Wisconsin Breast Cancer Dataset. The accuracy obtained by the proposed system is about 97%, recall is 99.58% and precision is about 93%. The experiments on this dataset yield higher performance as compared to the state‐of‐the‐art dataset. [ABSTRACT FROM AUTHOR]

Improving predictions of restoration outcomes is increasingly important to resource managers for accountability and adaptive management, yet there is limited guidance for selecting a predictive model from the multitude available. The goal of this article was to identify an optimal predictive framework for restoration ecology using 11 modeling frameworks (including machine learning, inferential, and ensemble approaches) and three data groups (field data, geographic data [GIS], and a combination thereof). We test this approach with a dataset from a large postfire sagebrush reestablishment project in the Great Basin, U.S.A. Predictive power varied among models and data groups, ranging from 58% to 79% accuracy. Finer‐scale field data generally had the greatest predictive power, although GIS data were present in the best models overall. An ensemble prediction computed from the 10 models parameterized to field data was well above average for accuracy but was outperformed by others that prioritized model parsimony by selecting predictor variables based on rankings of their importance among all candidate models. The variation in predictive power among a suite of modeling frameworks underscores the importance of a model comparison and refinement approach that evaluates multiple models and data groups, and selects variables based on their contribution to predictive power. The enhanced understanding of factors influencing restoration outcomes accomplished by this framework has the potential to aid the adaptive management process for improving future restoration outcomes. [ABSTRACT FROM AUTHOR]

Classical supervised methods like linear regression and decision trees are not completely adapted for identifying impacting factors on a response variable corresponding to zero‐inflated proportion data (ZIPD) that are dependent, continuous and bounded. In this article we propose a within‐block permutation‐based methodology to identify factors (discrete or continuous) that are significantly correlated with ZIPD, we propose a performance indicator quantifying the percentage of correlation explained by the subset of significant factors, and we show how to predict the ranks of the response variables conditionally on the observation of these factors. The methodology is illustrated on simulated data and on two real data sets dealing with epidemiology. In the first data set, ZIPD correspond to probabilities of transmission of Influenza between horses. In the second data set, ZIPD correspond to probabilities that geographic entities (eg, states and countries) have the same COVID‐19 mortality dynamics. [ABSTRACT FROM AUTHOR]

Conservation decision makers must negotiate social and technical complexities to achieve desired biodiversity outcomes. Quantitative models can inform decision making, by evaluating and predicting management outcomes, so that comparisons can be made between alternative courses of action. However, whether a proposed action is appropriate for implementation, regardless of its contribution to management outcomes, also requires consideration. Existing quantitative models have yet to fully incorporate the suitability of proposed management actions, which hinders their ability to inform decision making.We used gradient boosted decision trees – a machine‐learning technique – to determine the suitability of alternative management actions available to a biodiversity conservation programme. We demonstrate our approach using the Predator Free 2050 programme – a large and complex conservation initiative that seeks to eradicate selected invasive vertebrates from the entirety of New Zealand by 2050. We created a nationally contiguous network of management tools to suppress populations of invasive species across the entire country. We then used our suitability predictions to explore three scenarios for selecting invasive species management tools, based on maximising (a) implementation probability, (b) humaneness and (c) cost‐savings.Our models highlighted that an interplay of factors influence where management tools can potentially be implemented. Our management scenarios revealed what different contiguous management networks could look like for New Zealand over the next 10–15 years as an interim step to achieving Predator Free 2050. Each scenario differed in the tools selected for implementation in different places and in the overall economic costs associated with creating a contiguous management network. Some locations were identified as unsuitable for any existing management tools, indicating that future transformative technologies may be required to create a contiguous network.Synthesis and applications. Conservation decision making must not only consider biodiversity outcomes but also whether selected management actions are appropriate in the first place. Here, we used machine‐learning techniques to determine the suitability of competing managements actions that are proposed to meet biodiversity objectives. Our approach provides an objective, transparent and reproducible framework to determine the suitability of actions at sites across large spatial extents, under complex social and technical constraints. [ABSTRACT FROM AUTHOR]

The commutation failure is the most prevalent fault in line‐commutated converter based high‐voltage direct current systems, which may result in transient overvoltage on the sending‐side system. Overvoltage level evaluation has become a crucial task for power industries to assess the tripping risk of large‐scale wind turbines and implement effective stability control measures. In this paper, a derivation of the mathematical relationship between the reactive power consumed by the rectifier and AC voltage is presented firstly, along with an analytical expression for the peak value of transient overvoltage. Secondly, decision tree (DT) model is adopted to extract the mapping relationship between transient overvoltage and massive electrical quantities of power grids. The common DT algorithm is transformed by modifying the error weight assignment, which reflects the error tolerances for different actual overvoltage regions. On this basis, an overvoltage analysis method integrating the model‐driven and data‐driven techniques is proposed, and the improved DT algorithm is applied to fast error correction, enhancing the interpretability of regression prediction results. Case studies were performed in the simulation system of Northwest China local power grid with transient overvoltage problems, and the simulation results verified the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Causal inference practitioners have increasingly adopted machine learning techniques with the aim of producing principled uncertainty quantification for causal effects while minimizing the risk of model misspecification. Bayesian nonparametric approaches have attracted attention as well, both for their flexibility and their promise of providing natural uncertainty quantification. Priors on high‐dimensional or nonparametric spaces, however, can often unintentionally encode prior information that is at odds with substantive knowledge in causal inference—specifically, the regularization required for high‐dimensional Bayesian models to work can indirectly imply that the magnitude of the confounding is negligible. In this paper, we explain this problem and provide tools for (i) verifying that the prior distribution does not encode an inductive bias away from confounded models and (ii) verifying that the posterior distribution contains sufficient information to overcome this issue if it exists. We provide a proof‐of‐concept on simulated data from a high‐dimensional probit‐ridge regression model, and illustrate on a Bayesian nonparametric decision tree ensemble applied to a large medical expenditure survey. [ABSTRACT FROM AUTHOR]

Objective: To develop a comprehensive decision‐making tree for evaluating mid‐facial peri‐implant soft tissue dehiscence in the esthetic zone and provide a systematic approach for assessing various clinical case scenarios, determining appropriate treatment strategies, and considering factors such as the need for soft tissue augmentation, prosthetic changes, or implant removal. Clinical Considerations: This clinical decision tree illustrates numerous case scenarios with various esthetic complications around an esthetically compromised, but clinically healthy single implant and provides clinicians with possible solutions as a predictable map for horizontal and vertical soft tissue augmentation in order to manage different clinical circumstances. According to current evidence, the key to treating such esthetic complications is the use of an adequate pre‐surgical prosthetic interdisciplinary approach with proper surgical techniques in order to optimize soft tissue dimensions and create better esthetic results. This may be accomplished through a purely surgical, combination of surgical and prosthetic, or purely prosthetic approaches. Conclusions: The present report describes a series of successfully treated peri‐implant esthetic complication cases in accordance with the decision‐making tree that the authors recommend in order to achieve better long‐term esthetic outcomes. Clinical Significance: The combination of adequate pre‐surgical prosthetic interdisciplinary collaboration and proper surgical technique is critical in the optimization of sufficient soft tissue dimensions and contributes to a more highly esthetic result. This study demonstrates a clinical decision‐making tree to provide comprehensive, effective therapy of an esthetically compromised dental implant by using one of the following approaches: purely prosthetic, purely surgical, or a combination of surgical and prosthetic with or without abutment removal. [ABSTRACT FROM AUTHOR]

The aim of this pre‐ and post‐interventional study was to analyze the cost‐effectiveness of the multicomponent fall prevention program in hospitalized patients. To achieve this aim, cost‐effectiveness analysis performed using decision tree modeling was compared with the implementation of the fall prevention program and usual care. The primary outcome was the number of patient falls. The uncertainty in cost and effectiveness data was evaluated using one‐way sensitivity analysis, best–worst‐case scenario analysis, and probabilistic sensitivity analysis. According to cost‐effectiveness analysis, implementation of the fall prevention program was dominantly cost‐effective. As a result of the probabilistic sensitivity analysis, it was revealed that, even if willing‐to‐pay per‐fall prevented value was 0, the probability of being cost‐effective was 54.4% for the fall prevention program. Economic evaluation results showed that implementing the multicomponent fall prevention program was dominantly cost‐effective in hospitalized patients. Nurses and nurse managers can benefit from economic evaluations in their decision‐making processes to implement fall prevention programs. [ABSTRACT FROM AUTHOR]

Text classification (TC) is a very crucial task in this century of high‐volume text datasets. Feature selection (FS) is one of the most important stages in TC studies. In the literature, numerous feature selection methods are recommended for TC. In the TC domain, filter‐based FS methods are commonly utilized to select a more informative feature subsets. Each method uses a scoring system that is based on its algorithm to order the features. The classification process is then carried out by choosing the top‐N features. However, each method's feature order is distinct from the others. Each method selects by giving the qualities that are critical to its algorithm a high score, but it does not select by giving the features that are unimportant a low value. In this paper, we proposed a novel filter‐based FS method namely, brilliant probabilistic feature selector (BPFS), to assign a fair score and select informative features. While the BPFS method selects unique features, it also aims to select sparse features by assigning higher scores than common features. Extensive experimental studies using three effective classifiers decision tree (DT), support vector machines (SVM), and multinomial naive bayes (MNB) on four widely used datasets named Reuters‐21,578, 20Newsgroup, Enron1, and Polarity with different characteristics demonstrate the success of the BPFS method. For feature dimensions, 20, 50, 100, 200, 500, and 1000 dimensions were used. The experimental results on different benchmark datasets show that the BPFS method is more successful than the well‐known and recent FS methods according to Micro‐F1 and Macro‐F1 scores. [ABSTRACT FROM AUTHOR]

Soybean is one of the most important oilseed crops grown worldwide. However, abiotic stresses such as drought and salinity can seriously affect soybean production, especially in tropical climate conditions. To evaluate the adaptability and stability of soybean genotypes under abiotic stress conditions, some studies have proposed a multitrait tool to select stress‐tolerant soybean genotypes through a multitrait stability index (MTSI). This index can be used under stressful environmental conditions to quantify the genotypic stability of soybean cultivars. Our study is based on an unprecedented approach, where we propose to use a machine learning algorithm called 'Random Forest' to obtain a classification model based on a decision tree algorithm. The decision tree data structure can be used even by nonexperts facilitating the decision‐making process for genotype selection. The proposed model evaluated the importance of six shoot and root morphological variables and predicted from which controlled growth environment the soybean plants originated. Using this model more than 73% of the genotypic patterns were learned correctly. Besides that, this model can also predict and rank the most critical variables in the development of soybean genotypes, having obtained results very similar to recent field research. The research is important for plant breeders who seek an early selection of soybean seedlings for drought and saline stresses. [ABSTRACT FROM AUTHOR]

Objective: To determine the cost‐effectiveness of a virtual version of the Body Project (vBP), a cognitive dissonance‐based program, to prevent eating disorders (ED) among young women with a subjective sense of body dissatisfaction in the Swedish context. Method: A decision tree combined with a Markov model was developed to estimate the cost‐effectiveness of the vBP in a clinical trial population of 149 young women (mean age 17 years) with body image concerns. Treatment effect was modeled using data from a trial investigating the effects of vBP compared to expressive writing (EW) and a do‐nothing alternative. Population characteristics and intervention costs were sourced from the trial. Other parameters, including utilities, treatment costs for ED, and mortality were sourced from the literature. The model predicted the costs and quality‐adjusted life years (QALYs) related to the prevention of incidence of ED in the modeled population until they reached 25 years of age. The study used both a cost‐utility and return on investment (ROI) framework. Results: In total, vBP yielded lower costs and larger QALYs than the alternatives. The ROI analysis denoted a return of US $152 for every USD invested in vBP over 8 years against the do‐nothing alternative and US $105 against EW. Discussion: vBP is likely to be cost‐effective compared to both EW and a do‐nothing alternative. The ROI from vBP is substantial and could be attractive information for decision makers for implementation of this intervention for young females at risk of developing ED. Public Significance: This study estimates that the vBP is cost‐effective for the prevention of eating disorders among young women in the Swedish setting, and thus is a good investment of public resources. [ABSTRACT FROM AUTHOR]

The purpose of this paper was to utilize the decision trees technique to determine the factors responsible for high monthly milk yield in Polish Holstein-Friesian cows from 27 herds equipped with milking robots. The applied statistical method--the decision tree technique--showed that the most important factors responsible for monthly milk yield of dairy cows using robots were, in descending order of importance: milking frequency, lactation number, month of milking, and type of lying stall. At the same time, it has been ascertained that the highest monthly milk yield (47.24 kg) can be expected from multiparous cows kept in barns with a deep bedding that were milked more frequently than three times per day. On the other hand, the lowest milk production (13.56 kg) was observed among dairy cows milked less frequently than two times a day, with an average number of milked quarters lower than 3.97. The application of the decision trees technique allows a breeder to select appropriate levels of environmental factors and parameters that will help to ensure maximized milk production. [ABSTRACT FROM AUTHOR]

Personalized medicine aims at identifying best treatments for a patient with given characteristics. It has been shown in the literature that these methods can lead to great improvements in medicine compared to traditional methods prescribing the same treatment to all patients. Subgroup identification is a branch of personalized medicine, which aims at finding subgroups of the patients with similar characteristics for which some of the investigated treatments have a better effect than the other treatments. A number of approaches based on decision trees have been proposed to identify such subgroups, but most of them focus on two-arm trials (control/treatment) while a few methods consider quantitative treatments (defined by the dose). However, no subgroup identification method exists that can predict the best treatments in a scenario with a categorical set of treatments. We propose a novel method for subgroup identification in categorical treatment scenarios. This method outputs a decision tree showing the probabilities of a given treatment being the best for a given group of patients as well as labels showing the possible best treatments. The method is implemented in an R package psica available on CRAN. In addition to a simulation study, we present an analysis of a community-based nutrition intervention trial that justifies the validity of our method. [ABSTRACT FROM AUTHOR]

Abstract: Clinical practice guidelines (CPGs) and clinical pathways have become important tools for improving the uptake of evidence‐based care. Where CPGs are good, adherence to the recommendations within is thought to result in improved patient outcomes. However, the usefulness of such tools for improving patient important outcomes depends both on adherence to the guideline and whether or not the CPG in question is good. This begs the question of what it is that makes a CPG good? In this issue of the Journal, Djulbegovic and colleagues offer a theory to help guide the development of CPGs. The “fast‐and‐frugal tree” (FFT) heuristic theory is purported to provide the theoretical structure needed to quantitatively assess clinical guidelines in practice, something that the lack of theory to guide CPG development has precluded. In this paper, I examine the role of FFTs in providing an adequate theoretical framework for developing CPGs. In my view, positioning guideline development within the FFT framework may help with problems related to adherence. However, I believe that FTTs fall short in providing panel members with the theoretical basis needed to justify which factors should be considered when developing a CPG, how information on those factors derived from research studies should be interpreted, and how those factors should be integrated into the recommendation. [ABSTRACT FROM AUTHOR]

Rationale, Aims and Objectives: The development of clinical practice guidelines (CPG) suffers from the lack of an explicit and transparent framework for synthesising the key elements necessary to formulate practice recommendations. We matched deliberations of the American Society of Haematology (ASH) CPG panel for the management of pulmonary embolism (PE) with the corresponding decision‐theoretical constructs to assess agreement of the panel recommendations with explicit decision modelling. Methods: Five constructs were identified of which three were used to reformulate the panel's recommendations: (1) standard, expected utility threshold (EUT) decision model; (2) acceptable regret threshold model (ARg) to determine the frequency of tolerable false negative (FN) or false positive (FP) recommendations, and (3) fast‐and‐frugal tree (FFT) decision trees to formulate the entire strategy for management of PE. We compared four management strategies: withhold testing versus d‐dimer → computerized pulmonary angiography (CTPA) ('ASH‐Low') versus CTPA→ d‐dimer ('ASH‐High') versus treat without testing. Results: Different models generated different recommendations. For example, according to EUT, testing should be withheld for prior probability PE < 0.13%, a clinically untenable threshold which is up to 15 times (2/0.13) below the ASH guidelines threshold of ruling out PE (at post probability of PE ≤ 2%). Three models only agreed that the 'ASH low' strategy should be used for the range of pretest probabilities of PE between 0.13% and 13.27% and that the 'ASH high' management should be employed in a narrow range of the prior PE probabilities between 90.85% and 93.07%. For all other prior probabilities of PE, choosing one model did not ensure coherence with other models. Conclusions: CPG panels rely on various decision‐theoretical strategies to develop its recommendations. Decomposing CPG panels' deliberation can provide insights if the panels' deliberation retains a necessary coherence in developing guidelines. CPG recommendations often do not agree with the EUT decision analysis, widely used in medical decision‐making modelling. [ABSTRACT FROM AUTHOR]

Bupropion is widely used for the treatment of major depressive disorder and for smoking cessation assistance. Unfortunately, there are no practical systems to assist clinicians or poison centres in predicting outcomes based on clinical features. Hence, the purpose of this study was to use a decision tree approach to inform early diagnosis of outcomes secondary to bupropion overdose. This study utilized a dataset from the National Poison Data System, a 6‐year retrospective cohort study on toxic exposures and patient outcomes. A machine learning algorithm (decision tree) was applied to the dataset using the sci‐kit‐learn library in Python. Shapley Additive exPlanations (SHAP) were used as an explainable method. Comparative analysis was performed using random forest (RF), Gradient Boosting classification, eXtreme Gradient Boosting, Light Gradient Boosting (LGM) and voting ensembling. ROC curve and precision–recall curve were used to analyse the performance of each model. LGM and RF demonstrated the highest performance to predict outcome of bupropion exposure. Multiple seizures, conduction disturbance, intentional exposure, and confusion were the most influential factors to predict the outcome of bupropion exposure. Coma and seizure, including single, multiple and status, were the most important factors to predict major outcomes. [ABSTRACT FROM AUTHOR]

Improving corn (Zea mays L.) nitrogen (N) rate fertilizer recommendation tools can improve farmers' profits and mitigate N pollution. Numerous approaches have been tested to improve these tools, but to date improvements for predicting economically optimum N rate (EONR) have been modest. This work's objective was to use ensemble learning to improve our estimation of EONR (for a single at‐planting and split N application timing) by combining multiple corn N recommendation tools. The evaluation was conducted using 49 corn N response trials from eight states in the US Corn Belt and three growing seasons (2014–2016). Elastic net and decision tree approaches regressed EONR against three unique tools for each N application timing. Tools used in various combinations included a yield goal method, two soil nitrate tests (pre‐plant and late season), a computer simulation crop model (Maize‐N), and canopy reflectance sensing. Any combination of two or three N recommendation tools improved or maintained performance metrics (R2, root‐mean square error , and number of sites close to EONR). The best results for a single at‐planting recommendation occurred when combining the three at‐planting N recommendation tools (including interactions) with an elastic net regression model. This combined recommendation tool had a significant linear relationship with EONR (R2 = 0.46), an increase of 0.27 over the best tool evaluated alone. Combining multiple tools increased the implementation cost, but it did not reduce profitability and, sometimes, improved profitability. These results show tools can be combined to better match EONR, and thus could aid farmers in improving N management. Core Ideas: Individual corn N recommendation tools poorly estimated EONR (R2 ≤ 0.24) across many US Midwest states.Combining or "ensembling" two or more corn N recommendation tools improved EONR estimation (R2 ≤ 0.46).Both ensembling techniques evaluated proved effective at combining tools. [ABSTRACT FROM AUTHOR]

Decision trees provide an alternative to multivariate discriminant analysis, which is still the most commonly used in anthropometric studies. Our study analyzed the metric characterization of a recent virtual sample of 113 coxal bones using decision trees for sex determination. From 17 osteometric type I landmarks, a dataset was built with five classic distances traditionally reported in the literature and six new distances selected using the two-step ratio method. A ten-fold cross-validation was performed, and a decision tree was established on two subsamples (training and test sets). The decision tree established on the training set included three nodes and its application to the test set correctly classified 92% of individuals. This percentage was similar to the data of the literature. The usefulness of decision trees has been demonstrated in numerous fields. They have been already used in sex determination, body mass prediction, and ancestry estimation. This study shows another use of decision trees enabling simple and accurate sex determination. [ABSTRACT FROM AUTHOR]

This paper introduces a nonparametric regression approach for univariate and multivariate skewed responses using Bayesian additive regression trees (BART). Existing BART methods use ensembles of decision trees to model a mean function, and have become popular recently due to their high prediction accuracy and ease of use. The usual assumption of a univariate Gaussian error distribution, however, is restrictive in many biomedical applications. Motivated by an oral health study, we provide a useful extension of BART, the skewBART model, to address this problem. We then extend skewBART to allow for multivariate responses, with information shared across the decision trees associated with different responses within the same subject. The methodology accommodates within‐subject association, and allows varying skewness parameters for the varying multivariate responses. We illustrate the benefits of our multivariate skewBART proposal over existing alternatives via simulation studies and application to the oral health dataset with bivariate highly skewed responses. Our methodology is implementable via the R package skewBART, available on GitHub. [ABSTRACT FROM AUTHOR]

Background: Development of a macular sector‐wise decision tree model (DTM) for the prediction of parafoveal scotoma. Methods: This prospective study enrolled 126 patients with early‐stage open‐angle glaucoma (mean deviation ≥−6 decibels) without the signs of parafoveal scotoma on the 24‐2 visual field (VF) test (i.e., any abnormalities at the four innermost points). Based on the central 36 points of the 10‐2 pattern deviation plot, patients were classified as being with or without 10‐2 parafoveal scotoma. For the discrimination of patients from those without 10‐2 parafoveal scotoma, a macular ganglion cell‐inner plexiform layer (mGCIPL) sector‐wise DTM analysis was performed. Results: Among 126 eyes without 24‐2 parafoveal scotoma, 10‐2 parafoveal scotoma was detected in 77 (61.1%) eyes. The balanced accuracy of DTM was best in the inferotemporal sector (0.9286; 95% CI, 0.7458–0.9697) and worst in the inferior sector (0.8373; 0.6484–0.9204). DTM revealed that even in the absence of VF abnormalities at the innermost 4 points on the 24‐2 test, (1) 10‐2 parafoveal scotoma should be strongly suspected when the adjacent 24‐2 perifoveal point in the correlated sector is abnormal; (2) if the 24‐2 perifoveal point is normal, and if the probability colour codes of the correlated mGCIPL sector are green, the probability of 10‐2 parafoveal scotoma is very low. Conclusions: In clinical practice, the evaluation of the 24‐2 perifoveal test points along with the probability colour codes of mGCIPL can be a useful decision‐support tool in determining whether 10‐2 tests are needed for a given patient. [ABSTRACT FROM AUTHOR]

Accurately mapped locations within multi‐unit properties are useful for several organizations in today's society. Published work on geocoding methods either require detailed location reference data or does not apply to multi‐unit buildings. In this research, a generalizable method is realized to map apartment addresses to their explicit locations without access to indoor location reference data based on publicly available address‐ and geospatial‐building information. The performance of this approach is measured by conducting a comparative study between a linear interpolation baseline and gradient‐boosted decision trees model. The proposed method can successfully geocode addresses across different building shapes and sizes. Furthermore, the model significantly outperforms the baseline in terms of positional accuracy proving the feasibility of approximating apartment locations by their address‐ and geospatial‐building information. [ABSTRACT FROM AUTHOR]

BACKGROUND Knowledge of the sensory profile of coffee quality, associated with genetic and environmental factors, is of utmost importance for the international market, as well as for the productive sector. In this context, the goal of this study was to classify the quality of Coffea arabica L., cv. Yellow Bourbon, according to different scores obtained through sensory evaluations based on the Specialty Coffee Association of America protocol ( SCAA), and by means of decision trees resulting from applying the CHAID method (chi-square automatic interaction detection). To that end, we used a database with the sensory characteristics of cv. Yellow Bourbon and the environmental characteristics of the Mantiqueira de Minas region, State of Minas Gerais, Brazil. RESULTS The method used exhibited promising results regarding accuracy and success rates in order to discriminate coffee sensory quality as a function of the production environment. The results obtained clearly show the effect of the coffee growing environment on the Yellow Bourbon variety, resulting in notable sensory differences in the beverage. CONCLUSION It was possible to discriminate cv. Yellow Bourbon coffee samples, the sensory evaluations of which resulted in scores of ≥88 points, which are associated with growing environments at altitudes of ≥1200 m. © 2015 Society of Chemical Industry [ABSTRACT FROM AUTHOR]

Background: Before COVID‐19, people with intellectual disabilities were digitally excluded, facing difficulties accessing and using technology. The pandemic outbreak closed down many in‐person services and supports for this population. Methods: This study examined changes in technology use to support the healthcare and social connections of older adults with intellectual disability in Ireland (n = 682) during COVID‐19. Decision tree analyses explored factors associated with using technology for health and social connection; and to explore relationships between technology use and subjective outcomes of stress/anxiety and of positives during this period. Findings: Technology use for healthcare and social connection increased during COVID‐19, but not enough to make up for the loss of in‐person care/connection. Residence type was associated with increased use of technology for healthcare utilisation, with the highest rates among community group home residents and the lowest among those in independent/family settings. Increased technology use to speak with both family and friends was associated with prior digital skills and digital access. Increased technology use was significantly associated with both positive and negative experiences during the pandemic. Conclusions: The COVID‐19 pandemic offered an opportunity for increased digital connection for older adults with intellectual disabilities, but not all benefitted. Those with prior skills, available supports, and the greatest need to connect were more likely to enhance their use of technology. The complexity of the COVID‐19 period, including the general impact on mental health and well‐being, means that the role technology played in supporting quality of life and mitigating stress/anxiety needs further and longer‐term investigation. Accessible summary: Older adults with an intellectual disability in Ireland used technology more during the COVID‐19 pandemic—to use health services, and to speak to family and friends.Having digital skills before COVID‐19 helped people to increase their use of technology.Technology helped many people to stay connected, but did not fully make up for the loss of in‐person services and contact.Participants who used technology more during COVID‐19 were more likely to report some positive things during COVID‐19.Participants who used technology more were also more likely to feel stress or anxiety during COVID‐19.This shows that COVID‐19 was difficult for many people. But the study also shows that most people also found some good things during this time. [ABSTRACT FROM AUTHOR]

Young children appear not to grasp the independence between objective reality and subjective beliefs, as evidenced by their errors on false belief tasks. Whereas decades of research have examined children's developing understanding of the subjectivity of beliefs, however, almost no research has examined the other side of the issue: How do humans come to understand the objectivity of reality, and why is this understanding important? To help address this gap, this article proposes an evolutionary‐developmental account of how the understanding that reality is objective may have emerged in human thinking. Three key steps are highlighted: (i) phylogenetic foundations in great ape competitive mindreading, (ii) ontogenetic foundations in preverbal infant joint attention, and (iii) key experiences of perspectival conflict in linguistic humans. Functionally, the concept of an objective reality facilitated collaborative reasoning and joint decision‐making. To arrive at good joint decisions, individuals needed to recognize that both their own beliefs and others' beliefs could be wrong—with respect to the objective reality. [ABSTRACT FROM AUTHOR]

Coauthorship prediction applies predictive analytics to bibliographic data to predict authors who are highly likely to be coauthors. In this study, we propose an approach for coauthorship prediction based on bibliographic network embedding through a graph‐based bibliographic data model that can be used to model common bibliographic data, including papers, terms, sources, authors, departments, research interests, universities, and countries. A real‐world dataset released by AMiner that includes more than 2 million papers, 8 million citations, and 1.7 million authors were integrated into a large bibliographic network using the proposed bibliographic data model. Translation‐based methods were applied to the entities and relationships to generate their low‐dimensional embeddings while preserving their connectivity information in the original bibliographic network. We applied machine learning algorithms to embeddings that represent the coauthorship relationships of the two authors and achieved high prediction results. The reference model, which is the combination of a network embedding size of 100, the most basic translation‐based method, and a gradient boosting method achieved an F1 score of 0.9 and even higher scores are obtainable with different embedding sizes and more advanced embedding methods. Thus, the strengths of the proposed approach lie in its customizable components under a unified framework. [ABSTRACT FROM AUTHOR]

Objectives: The public health system faces major challenges due to the double burden of diabetes mellitus (DM) and tuberculosis (TB) in China. We aimed to investigate the prevalence and impact of diabetes on patients with TB. Methods: Stratified cluster sampling was used to select 13 counties as study sites in the Zhejiang province. Patients who visited designated TB hospitals in these areas participated in this study between 1 January 2017 and 28 February 2019. Multiple logistic regression models were performed to investigate the association between DM and bacteriological and imaging results. A decision tree was used to predict the bacteriology and imaging results under the influence of DM. Results: Of 5920 patients with newly diagnosed pulmonary tuberculosis, 643 (12.16%) had DM. Patients with pulmonary TB and DM were more likely to have pulmonary cavities (adjusted odds ratio [aOR], 2.81; 95% confidence intervals [95% CI]: 2.35–3.37) and higher rates of positive bacteriological tests (aOR, 2.32; 95% CI:1.87–2.87). Decision‐tree analysis showed similar results. Conclusions: Concurrence of DM and pulmonary TB makes patients more likely to have positive bacteriological results and pulmonary cavities. Therefore, appropriate measures are necessary to promptly identify and manage patients with TB and DM. [ABSTRACT FROM AUTHOR]

Decision trees trace their origins to the era of the early development of written records. This history illustrates a major strength of trees: exceptionally interpretable results which have an intuitive tree-like display which, in turn, enhances understanding and the dissemination of results. The computational origins of decision trees-sometimes called classification trees or regression trees-are models of biological and cognitive processes. This common heritage drives complementary developments of both statistical decision trees and trees designed for machine learning. The unfolding and progressive elucidation of the various features of trees throughout their early history in the late 20th century is discussed along with the important associated reference points and responsible authors. Statistical approaches, such as a hypothesis testing and various resampling approaches, have coevolved along with machine learning implementations. This had resulted in exceptionally adaptable decision tree tools, appropriate for various statistical and machine learning tasks, across various levels of measurement, with varying levels of data quality. Trees are robust in the presence of missing data and offer multiple ways of incorporating missing data in the resulting models. Although trees are powerful, they are also flexible and easy to use methods. This assures the production of high quality results that require few assumptions to deploy. The treatment ends with a discussion of the most current developments which continue to rely on the synergies and cross-fertilization between statistical and machine learning communities. Current developments with the emergence of multiple trees and the various resampling approaches that are employed are discussed. WIREs Comput Stat 2013, 5:448-455. doi: 10.1002/wics.1278 Conflict of interest: The authors have declared no conflicts of interest for this article. For further resources related to this article, please visit the WIREs website. [ABSTRACT FROM AUTHOR]

BACKGROUND: Previously, a lexicon and protocol for quantitative descriptive analysis (QDA) was established for the Uganda sweetpotato breeding program. The implication of QDA scores for priority sensory attributes on consumer preference should be determined to interpret results efficiently and make decisions effectively. The present study aimed to develop a gender‐responsive decision tree to obtain an overall sweetpotato eating quality score to facilitate demand‐led targeted breeding selection. It focused on Kamuli and Hoima districts (Uganda) and uses pre‐lease advanced clones ('NKB3', 'NKB105', 'NKB135', 'D11' and 'D20'), released varieties ('NASPOT 8' and 'NAROSPOT 1') and landraces ('Muwulu‐Aduduma', 'Umbrella'). RESULTS: Including boiled sweetpotato sensory characteristics, namely mealy, sweet taste, sweetpotato smell, firm and not fibrous, in breeding design would benefit end‐users, especially women given their role in varietal selection, food preparation and marketing. 'D20', 'NASPOT 8' and 'NAROSPOT 1' were most liked in both districts. 'NKB3' and 'D11' were the least liked in Hoima, whereas 'Muwulu‐Aduduma' was the least liked in Kamuli. There was a positive correlation between color and overall liking (r2 = 0.8) and consumers liked the color (average rating ≥ 6 on a nine‐point hedonic scale) of all genotypes. Threshold values (average rating on 11‐point scales) for consumer acceptability were identified (sweet taste = 6, sweetpotato aroma and flavor = 6, firmness = 3, and mealiness = 4). A regression decision tree tool was created to calculate an eating quality selection index when screening lines in breeding programs using the values. CONCLUSION: Decision trees that include consumer needs and gender considerations would facilitate demand‐led breeding and make varietal selection in sweetpotato breeding programs more effective. © 2023 The Authors. Journal of The Science of Food and Agriculture published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry. [ABSTRACT FROM AUTHOR]

Most of the methods that generate decision trees for a specific problem use the examples of data instances in the decision tree-generation process. This article proposes a method called RBDT-1-rule-based decision tree-for learning a decision tree from a set of decision rules that cover the data instances rather than from the data instances themselves. The goal is to create on demand a short and accurate decision tree from a stable or dynamically changing set of rules. The rules could be generated by an expert, by an inductive rule learning program that induces decision rules from the examples of decision instances such as AQ-type rule induction programs, or extracted from a tree generated by another method, such as the ID3 or C4.5. In terms of tree complexity (number of nodes and leaves in the decision tree), RBDT-1 compares favorably with AQDT-1 and AQDT-2, which are methods that create decision trees from rules. RBDT-1 also compares favorably with ID3 while it is as effective as C4.5 where both (ID3 and C4.5) are well-known methods that generate decision trees from data examples. Experiments show that the classification accuracies of the decision trees produced by all methods under comparison are indistinguishable. [ABSTRACT FROM AUTHOR]

The presented study applies the methods of data mining and prediction models to the subject of memory functioning in developmental dyslexia. This article sets forth the results of an analysis of the decision tree algorithm for the classification of dyslexia/non-dyslexia, based on frequency data from the modified simplified conjoint recognition experiment-a paradigm based on the fuzzy-trace theory used to investigate verbatim and gist memory. This decision tree model was created with the use of the C&RT algorithm, which makes a prediction of the classification with the use of four predictors: the numbers of different types of answers depending on the specific stimuli presented. Seventy-one high school students, 33 with developmental dyslexia, took part in a memory experiment. The model created using the decision tree algorithm has a very good overall validity. Excellent developmental dyslexia classification was accompanied by satisfactory non-dyslexia classification. The decision tree proposed predictors that are supported both theoretically and empirically. The results obtained show an important role of verbatim and gist memory functioning in developmental dyslexia and suggest that the pattern of performance observed in the memory tests can be used as a predictor of the developmental dyslexia disorder. Results encourage further usage of decision trees. [ABSTRACT FROM AUTHOR]

The Neural Decision Tree (NDT) is a hybrid supervised machine‐learning algorithm that combines the self‐limiting property of a decision tree (Classification and Regression Tree [CART]) algorithm with the artificial neural network. We demonstrate the use of NDT for a regression problem of building a prediction model for the plasmasphere electron density with solar and geomagnetic measurements as inputs. Our work replicates the work by Zhelavskaya et al. reported in their 2017 article (I. S. Zhelavskaya et al., 2017, https://doi.org/10.1002/2017JA024406) to show that NDT makes available sophisticated network layout for building a predictive model, thus taking advantage of deep‐learning potential of the neural network. We also demonstrate that with the ability to automatically select an appropriate network layout, as well as, effective initialization, the NDT algorithm allows research scientists in space weather to focus more of their attention on physically and statistically relevant aspects of using machine‐learning techniques. In fact, our example highlights the fact that the basic assumptions of standard supervise machine‐learning problems are often unsatisfied in real‐world space weather applications. Greater attention to these fundamental issues may create significantly different solutions to space weather forecast problems. Plain Language Summary: Machine learning techniques are widely applicable and have numerous breakthrough results in the space weather community. Decision Trees and Artificial Neural Networks are two of the most popular machine learning methods. We demonstrate the use of the technique called Neural Decision Tree (NDT), which is a hybrid method combining decision trees and neural networks, to predict plasmasphere electron density with solar and geomagnetic measurements as inputs. Our work replicates the work by Zhelavskaya et al. reported in their 2017 article to show that NDT makes available model layouts and takes advantage of both neural networks and decision trees. We provide a machine learning method to automatically construct a network‐based model. Such automation allows scientists to focus more on the physical and statistical aspects. Our example highlights the fact that the basic assumptions of standard supervise machine‐learning problems are often unsatisfied in real‐world space weather applications. Greater attention to these fundamental issues may create significantly different solutions to space weather forecast problems. Key Points: Neural Decision Tree (NDT) is an effective tool for building space weather forecast modelsMore elaborate neural network structure initialized using NDT can provide higher performance and training efficiencyPhysics‐based model constraints with statistical assumptions can have a significant impact on designing machine‐learning techniques [ABSTRACT FROM AUTHOR]