Random forest and gradient boosted decision trees (GBDT) are the two most commonly used machine learning algorithms. Both are ensemble models which means they combine many weak learners to get a strong one. Although both random forest and GBDT use the same weak learner, they are highly different algorithms. In this article, we will focus on 3 key differences between these ensemble techniques. Decision trees are used as the weak learner in both algorithms.

Random Forest (RFs) are among the most widely used Machine Learning (ML) classifiers. Even though RFs are not interpretable, there are no dedicated non-heuristic approaches for computing explanations of RFs. Moreover, there is recent work on polynomial algorithms for explaining ML models, including naive Bayes classifiers. Hence, one question is whether finding explanations of RFs can be solved in polynomial time. This paper answers this question negatively, by proving that computing one PI-explanation of an RF is D^P-complete. Furthermore, the paper proposes a propositional encoding for computing explanations of RFs, thus enabling finding PI-explanations with a SAT solver. This contrasts with earlier work on explaining boosted trees (BTs) and neural networks (NNs), which requires encodings based on SMT/MILP. Experimental results, obtained on a wide range of publicly available datasets, demontrate that the proposed SAT-based approach scales to RFs of sizes common in practical applications. Perhaps more importantly, the experimental results demonstrate that, for the vast majority of examples considered, the SAT-based approach proposed in this paper significantly outperforms existing heuristic approaches.

This article is motivated by the objective of providing a new analytically tractable and fully frequentist framework to characterize and implement regression trees while also allowing a multivariate (potentially high dimensional) response. The connection to regression trees is made by a high dimensional model with dynamic mean vectors over multi-dimensional change axes. Our theoretical analysis is carried out under a single two dimensional change point setting. An optimal rate of convergence of the proposed estimator is obtained, which in turn allows existence of limiting distributions. Distributional behavior of change point estimates are split into two distinct regimes, the limiting distributions under each regime is then characterized, in turn allowing construction of asymptotically valid confidence intervals for $2d$-location of change. All results are obtained under a high dimensional scaling $s\log^2 p=o(T_wT_h),$ where $p$ is the response dimension, $s$ is a sparsity parameter, and $T_w,T_h$ are sampling periods along change axes. We characterize full regression trees by defining a multiple multi-dimensional change point model. Natural extensions of the single $2d$-change point estimation methodology are provided. Two applications, first on segmentation of {\it Infra-red astronomy satellite (IRAS)} data and second to segmentation of digital images are provided. Methodology and theoretical results are supported with monte-carlo simulations.

The increasing concerns about data privacy and security drives the emergence of a new field of studying privacy-preserving machine learning from isolated data sources, i.e., \textit{federated learning}. Vertical federated learning, where different parties hold different features for common users, has a great potential of driving a more variety of business cooperation among enterprises in different fields. Decision tree models especially decision tree ensembles are a class of widely applied powerful machine learning models with high interpretability and modeling efficiency. However, the interpretability are compromised in these works such as SecureBoost since the feature names are not exposed to avoid possible data breaches due to the unprotected decision path. In this paper, we shall propose Fed-EINI, an efficient and interpretable inference framework for federated decision tree models with only one round of multi-party communication. We shall compute the candidate sets of leaf nodes based on the local data at each party in parallel, followed by securely computing the weight of the only leaf node in the intersection of the candidate sets. We propose to protect the decision path by the efficient additively homomorphic encryption method, which allows the disclosure of feature names and thus makes the federated decision trees interpretable. The advantages of Fed-EINI will be demonstrated through theoretical analysis and extensive numerical results. Experiments show that the inference efficiency is improved by over $50\%$ in average.

I want to expand this work into a series of µ-tutorial videos. If you're interested, please subscribe to my newsletter to stay in touch. It is not hard to be under an impression that the world is all about neural networks these days when it comes to making models. Many teams seem to brag about super-cool architectures as if getting enough quality data was straightforward, GPU racks were open 24/7 (for free), and their customer's patience was set to infinity. In this article, we will present one of the most basic machine-learning algorithms known as a Decision Tree.

Abstract-- Applications that need to sense, measure, and gather real-time information from the environment frequently face three main restrictions: power consumption, cost, and lack of infrastructure. Most of the challenges imposed by these limitations can be better addressed by embedding Machine Learning (ML) classifiers in the hardware that senses the environment, creating smart sensors able to interpret the low-level data stream. However, for this approach to be cost-effective, we need highly efficient classifiers suitable to execute in unresourceful hardware, such as low-power microcontrollers. In this paper, we present an open-source tool named EmbML - Embedded Machine Learning that implements a pipeline to develop classifiers for resource-constrained hardware. We describe its implementation details and provide a comprehensive analysis of its classifiers considering accuracy, classification time, and memory usage. Moreover, we compare the performance of its classifiers with classifiers produced by related tools to demonstrate that our tool provides a diverse set of classification algorithms that are both compact and accurate. Therefore, these smart sensors are more powerefficient since they eliminate the need for communicating all the raw data. PPLICATIONS that need to sense, measure, and gather real-time information from the environment frequently of interest - e.g., a dry soil crop area that needs watering or face three main restrictions [1]: power consumption, cost, the capture of a disease-vector mosquito.

A common approach for feature selection is to examine the variable importance scores for a machine learning model, as a way to understand which features are the most relevant for making predictions. Given the significance of feature selection, it is crucial for the calculated importance scores to reflect reality. Falsely overestimating the importance of irrelevant features can lead to false discoveries, while underestimating importance of relevant features may lead us to discard important features, resulting in poor model performance. Additionally, black-box models like XGBoost provide state-of-the art predictive performance, but cannot be easily understood by humans, and thus we rely on variable importance scores or methods for explainability like SHAP to offer insight into their behavior. In this paper, we investigate the performance of variable importance as a feature selection method across various black-box and interpretable machine learning methods. We compare the ability of CART, Optimal Trees, XGBoost and SHAP to correctly identify the relevant subset of variables across a number of experiments. The results show that regardless of whether we use the native variable importance method or SHAP, XGBoost fails to clearly distinguish between relevant and irrelevant features. On the other hand, the interpretable methods are able to correctly and efficiently identify irrelevant features, and thus offer significantly better performance for feature selection.

We give a quasipolynomial-time algorithm for learning stochastic decision trees that is optimally resilient to adversarial noise. Given an $\eta$-corrupted set of uniform random samples labeled by a size-$s$ stochastic decision tree, our algorithm runs in time $n^{O(\log(s/\varepsilon)/\varepsilon^2)}$ and returns a hypothesis with error within an additive $2\eta + \varepsilon$ of the Bayes optimal. An additive $2\eta$ is the information-theoretic minimum. Previously no non-trivial algorithm with a guarantee of $O(\eta) + \varepsilon$ was known, even for weaker noise models. Our algorithm is furthermore proper, returning a hypothesis that is itself a decision tree; previously no such algorithm was known even in the noiseless setting.

The current job survey shows that most software employees are planning to change their job role due to high pay for recent jobs such as data scientists, business analysts and artificial intelligence fields. The survey also indicated that work life imbalances, low pay, uneven shifts and many other factors also make employees think about changing their work life. In this paper, for an efficient organisation of the company in terms of human resources, the proposed system designed a model with the help of a random forest algorithm by considering different employee parameters. This helps the HR department retain the employee by identifying gaps and helping the organisation to run smoothly with a good employee retention ratio. This combination of HR and data science can help the productivity, collaboration and well-being of employees of the organisation. It also helps to develop strategies that have an impact on the performance of employees in terms of external and social factors.

AI - Artificial Intelligence AGI - Artificial General Intelligence ANN - Artificial Neural Network ANOVA - Analysis of Variance ANT - Actor Network Theory API - Application Programming Interface APX - Amsterdam Power Exchange AVE - Average Variance Extracted BU - Business Unit CART - Classification and Regression Tree CBMV - Crowd-based Business Model Validation CR - Composite Reliability CT - Computed Tomography CVC - Corporate Venture Capital DR - Design Requirement DP - Design Principle DSR - Design Science Research DSS - Decision Support System EEX - European Energy Exchange FsQCA - Fuzzy-Set Qualitative Comparative Analysis GUI - Graphical User Interface HI-DSS - Hybrid Intelligence Decision Support System HIT - Human Intelligence Task IoT - Internet of Things IS - Information System IT - Information Technology MCC - Matthews Correlation Coefficient ML - Machine Learning OCT - Opportunity Creation Theory OGEMA 2.0 - Open Gateway Energy Management 2.0 OS - Operating System R&D - Research & Development RE - Renewable Energies RQ - Research Question SVM - Support Vector Machine SSD - Solid-State Drive SDK - Software Development Kit TCP/IP - Transmission Control Protocol/Internet Protocol TCT - Transaction Cost Theory UI - User Interface VaR - Value at Risk VC - Venture Capital VPP - Virtual Power Plant Chapter I