Here's a new title that is a "must have" for any data scientist who uses the R language. It's a wonderful learning resource for tree-based techniques in statistical learning, one that's become my go-to text when I find the need to do a deep dive into various ML topic areas for my work. The methods discussed represent the cornerstone for using tabular data sets for making predictions using decision trees, ensemble methods like random forest, and of course the industry's darling gradient boosting machines (GBM). Algorithms like XGBoost are king of the hill for solving problems involving tabular data. A number of timely and somewhat high-profile benchmarks show that this class of algorithm beats deep learning algorithms for many problem domains.

This paper compares the performance of various data processing methods in terms of predictive performance for structured data. This paper also seeks to identify and recommend preprocessing methodologies for tree-based binary classification models, with a focus on eXtreme Gradient Boosting (XGBoost) models. Three data sets of various structures, interactions, and complexity were constructed, which were supplemented by a real-world data set from the Lending Club. We compare several methods for feature selection, categorical handling, and null imputation. Performance is assessed using relative comparisons among the chosen methodologies, including model prediction variability. This paper is presented by the three groups of preprocessing methodologies, with each section consisting of generalized observations. Each observation is accompanied by a recommendation of one or more preferred methodologies. Among feature selection methods, permutation-based feature importance, regularization, and XGBoost's feature importance by weight are not recommended. The correlation coefficient reduction also shows inferior performance. Instead, XGBoost importance by gain shows the most consistency and highest caliber of performance. Categorical featuring encoding methods show greater discrimination in performance among data set structures. While there was no universal "best" method, frequency encoding showed the greatest performance for the most complex data sets (Lending Club), but had the poorest performance for all synthetic (i.e., simpler) data sets. Finally, missing indicator imputation dominated in terms of performance among imputation methods, whereas tree imputation showed extremely poor and highly variable model performance.

In explainable machine learning, local post-hoc explanation algorithms and inherently interpretable models are often seen as competing approaches. This work offers a partial reconciliation between the two by establishing a correspondence between Shapley Values and Generalized Additive Models (GAMs). We introduce $n$-Shapley Values, a parametric family of local post-hoc explanation algorithms that explain individual predictions with interaction terms up to order $n$. By varying the parameter $n$, we obtain a sequence of explanations that covers the entire range from Shapley Values up to a uniquely determined decomposition of the function we want to explain. The relationship between $n$-Shapley Values and this decomposition offers a functionally-grounded characterization of Shapley Values, which highlights their limitations. We then show that $n$-Shapley Values, as well as the Shapley Taylor- and Faith-Shap interaction indices, recover GAMs with interaction terms up to order $n$. This implies that the original Shapely Values recover GAMs without variable interactions. Taken together, our results provide a precise characterization of Shapley Values as they are being used in explainable machine learning. They also offer a principled interpretation of partial dependence plots of Shapley Values in terms of the underlying functional decomposition. A package for the estimation of different interaction indices is available at \url{https://github.com/tml-tuebingen/nshap}.

We consider a global representation of a regression or classification function by decomposing it into the sum of main and interaction components of arbitrary order. We propose a new identification constraint that allows for the extraction of interventional SHAP values and partial dependence plots, thereby unifying local and global explanations. With our proposed identification, a feature's partial dependence plot corresponds to the main effect term plus the intercept. The interventional SHAP value of feature $k$ is a weighted sum of the main component and all interaction components that include $k$, with the weights given by the reciprocal of the component's dimension. This brings a new perspective to local explanations such as SHAP values which were previously motivated by game theory only. We show that the decomposition can be used to reduce direct and indirect bias by removing all components that include a protected feature. Lastly, we motivate a new measure of feature importance. In principle, our proposed functional decomposition can be applied to any machine learning model, but exact calculation is only feasible for low-dimensional structures or ensembles of those. We provide an algorithm and efficient implementation for gradient-boosted trees (xgboost) and random planted forest. Conducted experiments suggest that our method provides meaningful explanations and reveals interactions of higher orders. The proposed methods are implemented in an R package, available at \url{https://github.com/PlantedML/glex}.

Vehicle insurance claims size prediction needs methods to efficiently handle these claims. Machine learning (ML) is one of the methods that solve this problem. Tree-based ensemble learning algorithms are highly effective and widely used ML methods. This study considers how vehicle insurance providers incorporate ML methods in their companies and explores how the models can be applied to insurance big data. We utilize various tree-based ML methods, such as bagging, random forest, and gradient boosting, to determine the relative importance of predictors in predicting claims size and to explore the relationships between claims size and predictors. Furthermore, we evaluate and compare these models' performances. The results show that tree-based ensemble methods are better than the classical least square method. Keywords: claims size prediction; machine learning; tree-based ensemble methods; vehicle insurance.

Real-time traffic state estimation is essential for intelligent transportation systems. The NeurIPS 2022 Traffic4cast challenge provides an excellent testbed for benchmarking short-term traffic state estimation approaches. This technical report describes our solution to this challenge. In particular, we present an efficient two-stage gradient boosting framework for short-term traffic state estimation. The first stage derives the month, day of the week, and time slot index based on the sparse loop counter data, and the second stage predicts the future traffic states based on the sparse loop counter data and the derived month, day of the week, and time slot index. Experimental results demonstrate that our two-stage gradient boosting framework achieves strong empirical performance, achieving third place in both the core and the extended challenges while remaining highly efficient.

Port scanning is the process of attempting to connect to various network ports on a computing endpoint to determine which ports are open and which services are running on them. It is a common method used by hackers to identify vulnerabilities in a network or system. By determining which ports are open, an attacker can identify which services and applications are running on a device and potentially exploit any known vulnerabilities in those services. Consequently, it is important to detect port scanning because it is often the first step in a cyber attack. By identifying port scanning attempts, cybersecurity professionals can take proactive measures to protect the systems and networks before an attacker has a chance to exploit any vulnerabilities. Against this background, researchers have worked for over a decade to develop robust methods to detect port scanning. One such method revealed by a recent systematic review is the random forest supervised machine learning algorithm. The review revealed six existing studies using random forest since 2021. Unfortunately, those studies each exhibit different results, do not all use the same training and testing dataset, and only two include source code. Accordingly, the goal of this work was to reproduce the six random forest studies while addressing the apparent shortcomings. The outcomes are significant for researchers looking to explore random forest to detect port scanning and for practitioners interested in reliable technology to detect the early stages of cyber attack.

Random forests use an ensemble learning method for classification or regression. A random forest classifier is used to solve classification problems. When we train a random forest with training data, it generates several decision trees. And then, when input features are provided, the random forest selects the class that is selected by most of the trees in the random forest. In our previous articles, we discussed classification trees and regression trees.

This paper applies eXplainable Artificial Intelligence (XAI) methods to investigate the socioeconomic disparities in COVID patient mortality. An Extreme Gradient Boosting (XGBoost) prediction model is built based on a de-identified Austin area hospital dataset to predict the mortality of COVID-19 patients. We apply two XAI methods, Shapley Additive exPlanations (SHAP) and Locally Interpretable Model Agnostic Explanations (LIME), to compare the global and local interpretation of feature importance. This paper demonstrates the advantages of using XAI which shows the feature importance and decisive capability. Furthermore, we use the XAI methods to cross-validate their interpretations for individual patients. The XAI models reveal that Medicare financial class, older age, and gender have high impact on the mortality prediction. We find that LIME local interpretation does not show significant differences in feature importance comparing to SHAP, which suggests pattern confirmation. This paper demonstrates the importance of XAI methods in cross-validation of feature attributions.

An evident challenge ahead for the integrated circuit (IC) industry in the nanometer regime is the investigation and development of methods that can reduce the design complexity ensuing from growing process variations and curtail the turnaround time of chip manufacturing. Conventional methodologies employed for such tasks are largely manual; thus, time-consuming and resource-intensive. In contrast, the unique learning strategies of artificial intelligence (AI) provide numerous exciting automated approaches for handling complex and data-intensive tasks in very-large-scale integration (VLSI) design and testing. Employing AI and machine learning (ML) algorithms in VLSI design and manufacturing reduces the time and effort for understanding and processing the data within and across different abstraction levels via automated learning algorithms. It, in turn, improves the IC yield and reduces the manufacturing turnaround time. This paper thoroughly reviews the AI/ML automated approaches introduced in the past towards VLSI design and manufacturing. Moreover, we discuss the scope of AI/ML applications in the future at various abstraction levels to revolutionize the field of VLSI design, aiming for high-speed, highly intelligent, and efficient implementations.