Tree-boosting is a widely used machine learning technique for tabular data. However, its out-of-sample accuracy is critically dependent on multiple hyperparameters. In this article, we empirically compare several popular methods for hyperparameter optimization for tree-boosting including random grid search, the tree-structured Parzen estimator (TPE), Gaussian-process-based Bayesian optimization (GP-BO), Hyperband, the sequential model-based algorithm configuration (SMAC) method, and deterministic full grid search using $59$ regression and binary classification data sets. We find that the SMAC method clearly outperforms all the other considered methods on average, and it gives stable performance across a diverse collection of tabular data sets under a fixed tuning budget, which is relevant for users who cannot afford extensive manual trial-and-error tuning. We further observe that (i) a relatively large number of trials larger than $100$ is typically required for accurate tuning, (ii) using default values for hyperparameters or a full search over a small grid often yields very inaccurate models, (iii) all considered hyperparameters can have a material effect on the accuracy of tree-boosting, i.e., there is no small set of hyperparameters that is more important than others, and (iv) choosing the number of boosting iterations using early stopping yields more accurate results compared to including it in the search space for regression tasks.
Bayesian Optimization is a popular tool for tuning algorithms in automatic machine learning (AutoML) systems. Current state-of-the-art methods leverage Random Forests or Gaussian processes to build a surrogate model that predicts algorithm performance given a certain set of hyperparameter settings. In this paper, we propose a new surrogate model based on gradient boosting, where we use quantile regression to provide optimistic estimates of the performance of an unobserved hyperparameter setting, and combine this with a distance metric between unobserved and observed hyperparameter settings to …
Aggregating multiple learners through an ensemble of models aim to make better predictions by capturing the underlying distribution of the data more accurately. Different ensembling methods, such as bagging, boosting, and stacking/blending, have been studied and adopted extensively in research and practice. While bagging and boosting focus more on reducing variance and bias, respectively, stacking approaches target both by finding the optimal way to combine base learners. In stacking with the weighted average, ensembles are created from weighted averages of multiple base learners. It is known …
Amazon Web Services（亚马逊云科技）；Carnegie Mellon University（卡内基梅隆大学）
Given the increasing importance of machine learning (ML) in our lives, several algorithmic fairness techniques have been proposed to mitigate biases in the outcomes of the ML models. However, most of these techniques are specialized to cater to a single family of ML models and a specific definition of fairness, limiting their adaptibility in practice. We introduce a general constrained Bayesian optimization (BO) framework to optimize the performance of any ML model while enforcing one or multiple fairness constraints. BO is a model-agnostic optimization method that has been successfully applie…
Hyperparameter optimization is the process of identifying the appropriate hyperparameter configuration of a given machine learning model with regard to a given learning task. For smaller data sets, an exhaustive search is possible; However, when the data size and model complexity increase, the number of configuration evaluations becomes the main computational bottleneck. A promising paradigm for tackling this type of problem is surrogate-based optimization. The main idea underlying this paradigm considers an incrementally updated model of the relation between the hyperparameter space and the o…
Nested cross-validation when selecting classifiers is overzealous for most practical applications
University of Campinas（坎皮纳斯大学）；University of East Anglia（东英吉利大学）
当选择要应用于特定问题的分类算法时，必须同时选择该数据集的最佳算法\emph{and}和所选模型的最佳超参数集。通常的方法是应用嵌套交叉验证过程;在内部交叉验证中执行超参数选择，而外部交叉验证计算算法\emph{with cross-validation based hyperparameter tuning}的预期准确度的无偏估计。另一种方法，我们称之为“平面交叉验证”，使用一个单一的交叉验证步骤来选择最佳超参数值，并提供算法的预期准确度的估计，尽管有偏差，但仍然可以用于选择最佳学习算法。我们使用12种不同的算法对115个真实生活中的二进制数据集进行了测试，并得出结论，使用计算成本较低的平面交叉验证程序通常会导致选择一种算法，该算法对于所有实际目的而言，与通过嵌套交叉验证选择的算法具有相似的质量，前提是学习算法具有相对较少的超参数进行优化。
In the pharmaceutical industry, where it is common to generate many QSAR models with large numbers of molecules and descriptors, the best QSAR methods are those that can generate the most accurate predictions but that are also insensitive to hyperparameters and are computationally efficient. Here we compare Light Gradient Boosting Machine (LightGBM) to random forest, single-task deep neural nets, and Extreme Gradient Boosting (XGBoost) on 30 in-house data sets. While any boosting algorithm has many adjustable hyperparameters, we can define a set of standard hyperparameters at which LightGBM ma…
Cyberspace occupies a large portion of people's life in the age of modern technology, and while there are those who utilize it for good, there are also those who do not. Malware is an application whose construction was not motivated by a benign goal and it can harm, steal, or even alter personal information and secure applications and software. Thus, there are numerous techniques to avoid malware, one of which is to develop samples of malware so that the system can be updated with the growing number of malwares, allowing it to recognize when malwares attempt to enter. The Generative Adversaria…
On the sensitivity to hyperparameters of a Bayesian method to infer selection signatures
Universidad Nacional de Colombia, Sede Bogotá（哥伦比亚国立大学波哥大校区）；Corporación Colombiana de Investigación Agropecuaria, Sede Central（哥伦比亚农业研究公司总部）；Departament Ciències Matemàtiques i Informàtica de la Universitat de les Illes Balears（巴利阿里群岛大学数学与信息科学系）；Escuela de Matemáticas y Estadística, Universidad Pedagógica y Tecnológica de Colombia, Facultad Seccional Duitama（哥伦比亚教育与技术大学杜伊塔马分校数学与统计学院）；Departamento de Estadística, Universidad Nacional de Colombia, Sede Bogotá（哥伦比亚国立大学波哥大校区统计学系）
Click prediction boosting via Bayesian hyperparameter optimization based ensemble learning pipelines
Istanbul Technical University（伊斯坦布尔理工大学）；Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center（拉德堡德大学医学中心唐德斯脑、认知与行为研究所）；Kadir Has University（卡迪尔哈斯大学）
The work in ICML'09 showed that the derivatives of the classical multi-class logistic regression loss function could be re-written in terms of a pre-chosen "base class" and applied the new derivatives in the popular boosting framework. In order to make use of the new derivatives, one must have a strategy to identify/choose the base class at each boosting iteration. The idea of "adaptive base class boost" (ABC-Boost) in ICML'09, adopted a computationally expensive "exhaustive search" strategy for the base class at each iteration. It has been well demonstrated that ABC-Boost, when integrated wit…
Dana-Farber Cancer Institute（丹娜-法伯癌症研究所）；Harvard T.H. Chan School of Public Health（哈佛大学陈曾熙公共卫生学院）
Statistical learning models have been growing in popularity in recent years. Many of these models have hyperparameters that must be tuned for models to perform well. Tuning these parameters is not trivial. EZtune is an R package with a simple user interface that can tune support vector machines, adaboost, gradient boosting machines, and elastic net. We first provide a brief summary of the the models that EZtune can tune, including a discussion of each of their hyperparameters. We then compare the ease of using EZtune, caret, and tidymodels. This is followed with a comparison of the accuracy an…
Improved Differentially Private Regression via Gradient Boosting
Amazon AWS AI/ML（亚马逊AWS人工智能/机器学习部门）；University of Pennsylvania（宾夕法尼亚大学）；Columbia University（哥伦比亚大学）；University of California, Santa Barbara（加州大学圣巴巴拉分校）；Carnegie Mellon University（卡内基梅隆大学）
While the importance of prior selection is well understood, establishing guidelines for selecting priors in hierarchical models has remained an active, and sometimes contentious, area of Bayesian methodology research. Choices of hyperparameters for individual families of priors are often discussed in the literature, but rarely are different families of priors compared under similar models and hyperparameters. Using simulated data, we evaluate the performance of inverse gamma and half-$t$ priors for estimating the standard deviation of random effects in three hierarchical models: the 8-schools …
CoreML Group Apple Inc.（苹果公司CoreML团队）；Department of Mathematics Florida State University（佛罗里达州立大学数学系）；Department of Statistics Florida State University（佛罗里达州立大学统计系）
Binary Classification: Is Boosting stronger than Bagging?
Massachusetts Institute of Technology（麻省理工学院）；Operations Research Center, Massachusetts Institute of Technology（麻省理工学院运筹学中心）；Sloan School of Management, Massachusetts Institute of Technology（麻省理工学院斯隆管理学院）
本文提出 Enhanced Random Forests，通过自适应样本权重与个性化树权重，在 15 个二分类数据集上以默认超参数达到或超过 XGBoost，并利用树重要性恢复部分可解释性。
In recent years, a large number of XAI (eXplainable Artificial Intelligence) solutions have been proposed to explain existing ML (Machine Learning) models or to create interpretable ML models. Evaluation measures have recently been proposed and it is now possible to compare these XAI solutions. However, selecting the most relevant XAI solution among all this diversity is still a tedious task, especially when meeting specific needs and constraints. In this paper, we propose AutoXAI, a framework that recommends the best XAI solution and its hyperparameters according to specific XAI evaluation me…
Yale University（耶鲁大学）；Renmin University of China（中国人民大学）；JD Technology Group（京东科技集团）
Federated learning, conducive to solving data privacy and security problems, has attracted increasing attention recently. However, the existing federated boosting model sequentially builds a decision tree model with the weak base learner, resulting in redundant boosting steps and high interactive communication costs. In contrast, the federated bagging model saves time by building multi-decision trees in parallel, but it suffers from performance loss. With the aim of obtaining an outstanding performance with less time cost, we propose a novel model in a vertically federated setting termed as Fe…
Novel technologies in automated machine learning ease the complexity of algorithm selection and hyperparameter optimization. Hyperparameters are important for machine learning models as they significantly influence the performance of machine learning models. Many optimization techniques have achieved notable success in hyperparameter tuning and surpassed the performance of human experts. However, depending on such techniques as blackbox algorithms can leave machine learning practitioners without insight into the relative importance of different hyperparameters. In this paper, we consider build…
Columbia University（哥伦比亚大学）；Ludwig Maximilian University of Munich（慕尼黑路德维希-马克西米利安大学）
The performance of modern machine learning methods highly depends on their hyperparameter configurations. One simple way of selecting a configuration is to use default settings, often proposed along with the publication and implementation of a new algorithm. Those default values are usually chosen in an ad-hoc manner to work good enough on a wide variety of datasets. To address this problem, different automatic hyperparameter configuration algorithms have been proposed, which select an optimal configuration per dataset. This principled approach usually improves performance but adds additional …
Google Research（谷歌研究院）；Vector Institute（向量研究所）；University of Toronto（多伦多大学）；DeepMind（深度思维）；Institute for Advanced Study（普林斯顿高等研究院）
Selecting an optimizer is a central step in the contemporary deep learning pipeline. In this paper, we demonstrate the sensitivity of optimizer comparisons to the hyperparameter tuning protocol. Our findings suggest that the hyperparameter search space may be the single most important factor explaining the rankings obtained by recent empirical comparisons in the literature. In fact, we show that these results can be contradicted when hyperparameter search spaces are changed. As tuning effort grows without bound, more general optimizers should never underperform the ones they can approximate (i…
XGBoost, a scalable tree boosting algorithm, has proven effective for many prediction tasks of practical interest, especially using tabular datasets. Hyperparameter tuning can further improve the predictive performance, but unlike neural networks, full-batch training of many models on large datasets can be time consuming. Owing to the discovery that (i) there is a strong linear relation between dataset size & training time, (ii) XGBoost models satisfy the ranking hypothesis, and (iii) lower-fidelity models can discover promising hyperparameter configurations, we show that uniform subsampling m…
Despite the evolution of Convolutional Neural Networks (CNNs), their performance is surprisingly dependent on the choice of hyperparameters. However, it remains challenging to efficiently explore large hyperparameter search space due to the long training times of modern CNNs. Multi-fidelity optimization enables the exploration of more hyperparameter configurations given budget by early termination of unpromising configurations. However, it often results in selecting a sub-optimal configuration as training with the high-performing configuration typically converges slowly in an early phase. In t…
In addition to the best model architecture and hyperparameters, a full AutoML solution requires selecting appropriate hardware automatically. This can be framed as a multi-objective optimization problem: there is not a single best hardware configuration but a set of optimal ones achieving different trade-offs between cost and runtime. In practice, some choices may be overly costly or take days to train. To lift this burden, we adopt a multi-objective approach that selects and adapts the hardware configuration automatically alongside neural architectures and their hyperparameters. Our method bu…
Survey of Active Learning Hyperparameters: Insights from a Large-Scale Experimental Grid
Technische Universität Dresden（德累斯顿技术大学）；Technion - Israeli Institute of Technology（技术ion-以色列理工学院）；Hochschule für Technik und Wirtschaft Dresden（德累斯顿技术与经济高等学院）
IBM Thomas J. Watson Research Center（IBM托马斯·J·沃森研究中心）
We show, to our knowledge, the first theoretical treatments of two common questions in cross-validation based hyperparameter selection: (1) After selecting the best hyperparameter using a held-out set, we train the final model using {\em all} of the training data -- since this may or may not improve future generalization error, should one do this? (2) During optimization such as via SGD (stochastic gradient descent), we must set the optimization tolerance $ρ$ -- since it trades off predictive accuracy with computation cost, how should one set it? Toward these problems, we introduce the {\em ho…
The Graduate University for Advanced Studies (SOKENDAI)（综合研究大学院大学（SOKENDAI））；The Institute of Statistical Mathematics（统计数学研究所）
Deep Neural Networks inherit spurious correlations embedded in training data and hence may fail to predict desired labels on unseen domains (or environments), which have different distributions from the domain used in training. Invariance Learning (IL) has been developed recently to overcome this shortcoming; using training data in many domains, IL estimates such a predictor that is invariant to a change of domain. However, the requirement of training data in multiple domains is a strong restriction of IL, since it often needs high annotation cost. We propose a novel IL framework to overcome t…
Vector Institute（向量研究所）；Mines Paris Université PSL（巴黎文理研究大学巴黎矿业学院）；Google Research（谷歌研究院）
Models need to be trained with privacy-preserving learning algorithms to prevent leakage of possibly sensitive information contained in their training data. However, canonical algorithms like differentially private stochastic gradient descent (DP-SGD) do not benefit from model scale in the same way as non-private learning. This manifests itself in the form of unappealing tradeoffs between privacy and utility (accuracy) when using DP-SGD on complex tasks. To remediate this tension, a paradigm is emerging: fine-tuning with differential privacy from a model pretrained on public (i.e., non-sensiti…
An adaptively inexact first-order method for bilevel optimization with application to hyperparameter learning
EPSRC Centre for Doctoral Training in Statistical Applied Mathematics at Bath (SAMBa)（EPSRC统计应用数学博士培训中心（SAMBa））；Department of Mathematical Sciences, University of Bath（巴斯大学数学科学系）；Department of Electronics & Electrical Communication Engineering, Indian Institute of Technology (IIT), Kharagpur（印度理工学院（Kharagpur）电子与电气通信工程系）；School of Mathematics and Statistics, University of Sydney（悉尼大学数学与统计学学院）
Time-Efficient Hybrid Hyperparameter Tuning Approach for Cardiovascular Disease Classification
Department of Computer Science, Institute of Science, Banaras Hindu University（计算机科学系，科学学院，班纳拉森胡大学）；School of Computer Science, University of Petroleum and Energy Studies（计算机科学学院，石油与能源研究大学）
