## Randomized Trees(隨機樹)到底是什麼？

Randomized Trees 隨機樹 - In this work, several machine learning (ML) models are empirically evaluated on their estimation accuracy for the task of predicting latent high-dynamic magnet temperature profiles, specifically, ordinary least squares, support vector regression, $k$-nearest neighbors, randomized trees, and neural networks.^{[1]}Random Forest (forest of randomized trees, a tree ensemble) algorithm is considered for the performance evaluation, as tree model supports concurrency and all trees are grown simultaneously in it, so it is a suitable parallel approach with good accuracy, noisy & imbalance dataset handling capability and also it never overfit unlike a single tree model for large dataset.

^{[2]}The proposed approach first builds an ensemble of randomized trees in order to gather information on the hierarchy of features and their separability among the classes.

^{[3]}

在這項工作中，對幾種機器學習 (ML) 模型的估計精度進行了經驗評估，以預測潛在的高動態磁體溫度分佈，特別是普通最小二乘法、支持向量回歸、$k$-最近鄰、隨機樹和神經網絡。

^{[1]}隨機森林（隨機樹的森林，樹集成）算法被考慮用於性能評估，因為樹模型支持並發並且所有樹都在其中同時生長，因此它是一種合適的並行方法，具有良好的準確性、噪聲和不平衡數據集處理能力，而且它永遠不會過擬合，不像大型數據集的單個樹模型。

^{[2]}所提出的方法首先構建一個隨機樹的集合，以收集有關特徵層次結構及其類之間可分離性的信息。

^{[3]}

## support vector machine 支持向量機

0 tree, extremely randomized trees (ET), weighted k-nearest neighbors (KKNN), artificial neural networks (ANN), random forest (RF), support vector machine (SVM) with linear and radial kernels and extreme gradient boosting trees (XGBoost).^{[1]}Various AI approaches are useful for peptide-based drug discovery, such as support vector machine, random forest, extremely randomized trees, and other more recently developed deep learning methods.

^{[2]}A total of 5 ML-based algorithms, including a support vector machine, logistic regression, extremely randomized trees, a convolutional neural network, and a recurrent neural network designed to identify vaccine misinformation, were evaluated for identification performance.

^{[3]}Methods Extremely randomized trees (ERT), support vector machines, multinomial logistic regression, and K-nearest neighbor were applied, and performances were evaluated by cross-validation.

^{[4]}The methods used are logistic regression (LR), support vector machines (SVM), neural networks (NN) in the fully connected multi-layer perceptron (MLP) implementation, random forests (RF), decision trees (DTs), extremely randomized trees (XT) and extreme gradient boosting (XGB).

^{[5]}Support vector machine and extremely randomized trees were used to build the RM.

^{[6]}

0 樹、極端隨機樹 (ET)、加權 k 最近鄰 (KKNN)、人工神經網絡 (ANN)、隨機森林 (RF)、具有線性和徑向內核的支持向量機 (SVM) 以及極端梯度提升樹 (XGBoost ）。

^{[1]}各種 AI 方法可用於基於肽的藥物發現，例如支持向量機、隨機森林、極度隨機樹和其他最近開發的深度學習方法。

^{[2]}nan

^{[3]}nan

^{[4]}nan

^{[5]}nan

^{[6]}

## support vector regression

We apply the ML algorithms extremely randomized trees (ExtraTrees), adaptive boosting (AdaBoost), and support vector regression (SVR) to this problem because of their ability to deal with low data volumes and their low processing times.^{[1]}Motivated by the increasing interest in the application of machine learning techniques for power system control and demand response applications, this paper presents a benchmark of regression methods (extremely randomized trees (extra-trees), multi-layer perceptron (MLP), extreme gradient boosting, light gradient boosting machines, support vector regression (SVR) and extreme learning machines (ELMs)) available for function approximation in reinforcement learning (RL) techniques.

^{[2]}We use ridge regression, kernel ridge regression, k-nearest neighors, support vector regression, AdaBoost (Freund and Schapire, 1997), gradient tree boosting, gaussian process regressor, extremely randomized trees (Geurts et al.

^{[3]}

我們將 ML 算法極其隨機的樹 (ExtraTrees)、自適應提升 (AdaBoost) 和支持向量回歸 (SVR) 應用於此問題，因為它們能夠處理低數據量和低處理時間。

^{[1]}由於人們對將機器學習技術應用於電力系統控制和需求響應應用的興趣日益濃厚，本文提出了回歸方法的基準（極端隨機樹（extra-trees）、多層感知器（MLP）、極端梯度提升、光梯度增強機、支持向量回歸 (SVR) 和極限學習機 (ELM)) 可用於強化學習 (RL) 技術中的函數逼近。

^{[2]}nan

^{[3]}

## multi layer perceptron 多層感知器

The machine learning techniques were random forest (RF), extremely randomized trees (extra-tree), deep reinforcement learning (DRL), time series forecasting (TSF), multi-layer perceptron (MLP), k-nearest neighbor (KNN) and logistic regression (LR).^{[1]}More specifically, StackIL6 was constructed from twelve different feature descriptors derived from three major groups of features (composition-based features, composition-transition-distribution-based features and physicochemical properties-based features) and five popular machine learning algorithms (extremely randomized trees, logistic regression, multi-layer perceptron, support vector machine and random forest).

^{[2]}In addition, we evaluate our proposed method in comparison with traditional methods such as Decision Tree, Multi-layer Perceptron, Extremely randomized trees, Random Forest, and k-Nearest Neighbour on a specific dataset, WISDM.

^{[3]}

機器學習技術包括隨機森林 (RF)、極度隨機樹 (extra-tree)、深度強化學習 (DRL)、時間序列預測 (TSF)、多層感知器 (MLP)、k 最近鄰 (KNN) 和邏輯回歸（LR）。

^{[1]}更具體地說，StackIL6 由 12 個不同的特徵描述符構建而成，這些描述符源自三大特徵組（基於成分的特徵、基於成分-過渡-分佈的特徵和基於物理化學性質的特徵）和五種流行的機器學習算法（極度隨機樹、邏輯回歸、多層感知器、支持向量機和隨機森林）。

^{[2]}nan

^{[3]}

## Extremely Randomized Trees 極度隨機的樹

In the cost‐sensitive stacked generalization (CSSG) approach, logistic regression (LR) and extremely randomized trees classifiers in cases of CSL and cost‐insensitive are used as a final classifier of stacking scheme.^{[1]}The original spectroscopic data was first pre-treated using the multiplicative scatter correction (MSC) method and 4 principal components were extracted using extremely randomized trees (Extra-trees) and principal component analysis (PCA) algorithms, and different kinds of classification models were established.

^{[2]}Finally, we ensemble XGBoost, random forest, and extremely randomized trees to construct deep forest model via cascade architecture for PPIs prediction (GcForest-PPI).

^{[3]}To solve the problem of imbalanced classification in wind turbine generator fault detection, a cost-sensitive extremely randomized trees (CS-ERT) algorithm is proposed in this paper, in which the cost-sensitive learning method is introduced into an extremely randomized trees (ERT) algorithm.

^{[4]}Then feature extraction and selection are conducted through comparative principal component analysis (PCA) and extremely randomized trees (ET) algorithms.

^{[5]}The results show that stacked ensemble (SE) models are superior to models based on five supervised-learning algorithms, including gradient boosting machine (GBM), generalized linear model (GLM), distributed random forest (DRF), deep learning (DL), and extremely randomized trees (XRT).

^{[6]}For the internal disorder detection (browning), a classification benchmark composed by five different models (PLS-LDA, PCA-Logistic Regression, PCA-Extremely Randomized Trees, Extremely Randomized Trees and SVC) was implemented.

^{[7]}ML methodologies (Random Forests, Extremely Randomized Trees, and Boosted Trees, Logistic Regression) were adopted, obtaining high values of accuracy: all report an accuracy above 75%.

^{[8]}Hence, we utilize tree-based machine learning algorithms, decision trees, gradient boosting, and extremely randomized trees to assess the variable importance.

^{[9]}In addition, compared to Random Forest and Extremely Randomized Trees, Essence Random Forest better leverages the value of unstructured data by offering an enhanced churn detection regardless of the addressed perspective i.

^{[10]}RESULTS As final model, we propose a set of Extremely Randomized Trees classifiers considering 27 features, including creatinine level, urea, red blood cells count, eGFR trend (which is not even the most important), age and associated comorbidities.

^{[11]}In this paper, we firstly investigated the capability of an Extremely Randomized Trees Fusion Model (ERTFM) to reconstruct high spatiotemporal resolution reflectance data from a fusion of the Chinese GaoFen-1 (GF-1) and the Moderate Resolution Imaging Spectroradiometer (MODIS) products.

^{[12]}Best results are achieved with the Extremely Randomized Trees classifier with a mean test score on the hold out set of 92.

^{[13]}This work then introduces useful new tools, based on Random Forest (RF) and Extremely Randomized Trees or Extra Trees (ET) algorithms to classify breast cancer.

^{[14]}Using random forests and extremely randomized trees, with mean decrease impurity, mean decrease accuracy and SHapley Additive exPlanations feature importance methods, prediction accuracy is consistent across methods for US and global firms.

^{[15]}In this paper, we develop a model named iPromoter-ET using the k-mer nucleotide composition, binary encoding and dinucleotide property matrix-based distance transformation for features extraction, and extremely randomized trees (extra trees) for feature selection.

^{[16]}0 tree, extremely randomized trees (ET), weighted k-nearest neighbors (KKNN), artificial neural networks (ANN), random forest (RF), support vector machine (SVM) with linear and radial kernels and extreme gradient boosting trees (XGBoost).

^{[17]}To detect and diagnose the faults in timely manner, we adopt an ensemble learning-based lightweight technique called Extremely Randomized Trees or Extra-Trees.

^{[18]}The aim of this paper is to propose a novel prediction model based on an ensemble of deep neural networks adapting the extremely randomized trees method originally developed for random forests.

^{[19]}In this paper, a prediction model for positive switching impulse breakdown voltage of rod-plane air gap based on extremely randomized trees is proposed.

^{[20]}The machine learning techniques were random forest (RF), extremely randomized trees (extra-tree), deep reinforcement learning (DRL), time series forecasting (TSF), multi-layer perceptron (MLP), k-nearest neighbor (KNN) and logistic regression (LR).

^{[21]}More specifically, StackIL6 was constructed from twelve different feature descriptors derived from three major groups of features (composition-based features, composition-transition-distribution-based features and physicochemical properties-based features) and five popular machine learning algorithms (extremely randomized trees, logistic regression, multi-layer perceptron, support vector machine and random forest).

^{[22]}Various AI approaches are useful for peptide-based drug discovery, such as support vector machine, random forest, extremely randomized trees, and other more recently developed deep learning methods.

^{[23]}We attempted to map the dust emission prone (DEP) areas in this region of Iran using the most accurate model among the random forest (RF), conditional RF (CRF), parallel RF (PRF), and extremely randomized trees (ERT) models.

^{[24]}A total of 5 ML-based algorithms, including a support vector machine, logistic regression, extremely randomized trees, a convolutional neural network, and a recurrent neural network designed to identify vaccine misinformation, were evaluated for identification performance.

^{[25]}Methods Extremely randomized trees (ERT), support vector machines, multinomial logistic regression, and K-nearest neighbor were applied, and performances were evaluated by cross-validation.

^{[26]}, linear discriminant analysis (LDA) and extremely randomized trees (ERT)), is used for the detection of honey adulteration with glucose syrup.

^{[27]}This study aimed to evaluate the performance of multivariate adaptive regression splines (MARS) and extremely randomized trees (ERT) models for predicting the internal and external dust events frequencies (DEF) across the northeastern and southwestern regions of the Gavkhouni International Wetland.

^{[28]}A multi-model comparison revealed that for urban land use classification with high-dimensional features, the multi-layer stacking ensemble models achieved better performance than base models such as random forest, extremely randomized trees, LightGBM, CatBoost, and neural networks.

^{[29]}The methods used are logistic regression (LR), support vector machines (SVM), neural networks (NN) in the fully connected multi-layer perceptron (MLP) implementation, random forests (RF), decision trees (DTs), extremely randomized trees (XT) and extreme gradient boosting (XGB).

^{[30]}Based on 220 data sets with binary outcomes, diversity forests are compared with conventional random forests and random forests using extremely randomized trees.

^{[31]}A blending algorithm that consists of random forests (RFs), extremely randomized trees (Extra-Trees), and gradient boosting decision trees (GBDTs) is finally adopted for feature learning and epileptic signal classification.

^{[32]}Eleven Machine learning models including Multiple Linear Regression (MLR), Ridge and Lasso regression; Support Vector Regression (SVR), ANN as well as Classification and Regression Tree (CART) based algorithms including Decision Trees, Random Forest, eXtreme Gradient Boosting (XGBoost), Gradient Boosting and Extremely Randomized Trees (ERT), are applied on a dataset consisting of 202 datapoints.

^{[33]}It starts with the Synthetic Minority Oversampling Technique (SMOTE) method to solve the imbalanced classes problem in the dataset and then selects the important features for each class existing in the dataset by the Gini Impurity criterion using the Extremely Randomized Trees Classifier (Extra Trees Classifier).

^{[34]}In addition, we evaluate our proposed method in comparison with traditional methods such as Decision Tree, Multi-layer Perceptron, Extremely randomized trees, Random Forest, and k-Nearest Neighbour on a specific dataset, WISDM.

^{[35]}Finally, a hybrid classification model is proposed based on fast independent component analysis (ICA) and extremely randomized trees (ET).

^{[36]}, space-time extremely randomized trees, denoted as the STET model), is designed to estimate near-surface PM10 concentrations.

^{[37]}In this study, three common open-access satellite image datasets (Sentinel-2B, Landsat-8, and Gaofen-6) were used for extracting information on rocky desertification in a typical karst region (Guangnan County, Yunnan) of southwest China, using three machine-learning algorithms implemented in the Python programming language: random forest (RF), bagged decision tree (BDT), and extremely randomized trees (ERT).

^{[38]}Bacterial, viral and clinical data were subsequently used as inputs for extremely randomized trees classification models aiming to distinguish subjects with CAP from healthy controls.

^{[39]}In this work, we introduce a new machine learning (ML) based scoring function called ET‐Score, which employs the distance‐weighted interatomic contacts between atom type pairs of the ligand and the protein for featurizing protein−ligand complexes and Extremely Randomized Trees algorithm for the training process.

^{[40]}The extremely randomized trees method provided robust performance with highest accuracy and well-balanced sensitivity and specificity (accuracy 73%, sensitivity 72%, specificity 75%, positive predictive value 33%, negative predictive value 94%, area under the curve 81%).

^{[41]}We analyzed further correlations by applying Logistic Regression and seven machine learning techniques (Decision Tree, Random Forest, Extremely Randomized Trees, AdaBoost, Gradient Boosting, XGBoost).

^{[42]}We develop a two-stage diagnostic classification system for psychotic disorders using an extremely randomized trees machine learning algorithm.

^{[43]}In this paper, we extend the distributed Extremely Randomized Trees (ERT) approach w.

^{[44]}Extremely randomized trees analysis showed that PM10 was the main influencing factor for corrosion of portland, copper, cast bronze, and carbon steel with a relative importance of 0.

^{[45]}(2) by using the improved vgg19 to extract the eigenvalues of tumors, the fully connected layer is replaced by extremely randomized trees for classification.

^{[46]}This paper proposes a data recovery algorithm based on the Attribute Correlation and Extremely randomized Trees (ACET).

^{[47]}The ELBAD ensemble learning algorithm is significantly superior to other state-of-the-art popular ensemble learning algorithms, including AdaBoost, Bagging, Decorate, extremely randomized trees (ET), gradient boosting decision tree (GBDT), random forest (RF), and rotation forest (RoF) on 30 UCI datasets.

^{[48]}In this paper, we consider the classification problem and show how the Extremely Randomized Trees (ERT) algorithm could be adapted for settings where (structured) data is distributed over multiple sources.

^{[49]}Support vector machine and extremely randomized trees were used to build the RM.

^{[50]}

在成本敏感堆疊泛化 (CSSG) 方法中，邏輯回歸 (LR) 和 CSL 和成本不敏感情況下的極端隨機樹分類器被用作堆疊方案的最終分類器。

^{[1]}原始光譜數據首先使用乘法散射校正（MSC）方法進行預處理，並使用極端隨機樹（Extra-trees）和主成分分析（PCA）算法提取4個主成分，並建立不同種類的分類模型.

^{[2]}nan

^{[3]}nan

^{[4]}nan

^{[5]}nan

^{[6]}nan

^{[7]}nan

^{[8]}nan

^{[9]}nan

^{[10]}nan

^{[11]}nan

^{[12]}nan

^{[13]}nan

^{[14]}nan

^{[15]}nan

^{[16]}0 樹、極端隨機樹 (ET)、加權 k 最近鄰 (KKNN)、人工神經網絡 (ANN)、隨機森林 (RF)、具有線性和徑向內核的支持向量機 (SVM) 以及極端梯度提升樹 (XGBoost ）。

^{[17]}nan

^{[18]}本文的目的是提出一種基於深度神經網絡集合的新型預測模型，該模型採用最初為隨機森林開發的極端隨機樹方法。

^{[19]}nan

^{[20]}機器學習技術包括隨機森林 (RF)、極度隨機樹 (extra-tree)、深度強化學習 (DRL)、時間序列預測 (TSF)、多層感知器 (MLP)、k 最近鄰 (KNN) 和邏輯回歸（LR）。

^{[21]}更具體地說，StackIL6 由 12 個不同的特徵描述符構建而成，這些描述符源自三大特徵組（基於成分的特徵、基於成分-過渡-分佈的特徵和基於物理化學性質的特徵）和五種流行的機器學習算法（極度隨機樹、邏輯回歸、多層感知器、支持向量機和隨機森林）。

^{[22]}各種 AI 方法可用於基於肽的藥物發現，例如支持向量機、隨機森林、極度隨機樹和其他最近開發的深度學習方法。

^{[23]}nan

^{[24]}nan

^{[25]}nan

^{[26]}nan

^{[27]}nan

^{[28]}nan

^{[29]}nan

^{[30]}nan

^{[31]}nan

^{[32]}nan

^{[33]}nan

^{[34]}nan

^{[35]}nan

^{[36]}nan

^{[37]}nan

^{[38]}nan

^{[39]}在這項工作中，我們引入了一種新的基於機器學習 (ML) 的評分函數，稱為 ET-Score，它利用配體和蛋白質的原子類型對之間的距離加權原子間接觸來表徵蛋白質-配體複合物和極隨機樹算法對於訓練過程。

^{[40]}極端隨機樹方法提供了穩健的性能，具有最高的準確性和平衡的敏感性和特異性（準確性 73%、敏感性 72%、特異性 75%、陽性預測值 33%、陰性預測值 94%、曲線下面積 81%） .

^{[41]}nan

^{[42]}nan

^{[43]}nan

^{[44]}nan

^{[45]}nan

^{[46]}nan

^{[47]}nan

^{[48]}nan

^{[49]}nan

^{[50]}

## Extra Randomized Trees

In the CSSG approach, the logistic regression classifier and extra randomized trees ensemble method in cost-sensitive learning and cost-insensitive conditions are employed as a final classifier of stacking scheme.^{[1]}We apply multiple machine learning algorithms: Logistic Regression (LR), Ridge Regression (RR), Support Vector Machine (SVM), Random Forest (RF), Extra Randomized Trees (ET) and Long Short-Term Memory (LSTM) to a collection of Bengali corpus and corresponding machine translated English version.

^{[2]}

在 CSSG 方法中，採用代價敏感學習和代價不敏感條件下的邏輯回歸分類器和額外隨機樹集成方法作為堆疊方案的最終分類器。

^{[1]}nan

^{[2]}

## randomized trees classifier 隨機樹分類器

In the cost‐sensitive stacked generalization (CSSG) approach, logistic regression (LR) and extremely randomized trees classifiers in cases of CSL and cost‐insensitive are used as a final classifier of stacking scheme.^{[1]}At that point, we utilized Recursive Feature Elimination with Cross-Validation, which conglomerate direct SVC, Random decision Forest Classifier, Extremely-Randomized Trees Classifier, Adobos-Classifier, and Multivariate Event model Classifier as assessor individually, to choose hearty highlights imperative to brain ischemia subgrouping.

^{[2]}RESULTS As final model, we propose a set of Extremely Randomized Trees classifiers considering 27 features, including creatinine level, urea, red blood cells count, eGFR trend (which is not even the most important), age and associated comorbidities.

^{[3]}Best results are achieved with the Extremely Randomized Trees classifier with a mean test score on the hold out set of 92.

^{[4]}It starts with the Synthetic Minority Oversampling Technique (SMOTE) method to solve the imbalanced classes problem in the dataset and then selects the important features for each class existing in the dataset by the Gini Impurity criterion using the Extremely Randomized Trees Classifier (Extra Trees Classifier).

^{[5]}Several classifiers are implemented and comparison of the results shows that Extremely Randomized Trees classifier produces the best results.

^{[6]}Fifty features with the highest ANOVA F-score were selected and fed to an extremely randomized trees classifier.

^{[7]}For the classification stage, two classifiers where tested, the Extremely Randomized Trees classifier (ET) [5], and the Adaboost (ADB) [6].

^{[8]}

在成本敏感堆疊泛化 (CSSG) 方法中，邏輯回歸 (LR) 和 CSL 和成本不敏感情況下的極端隨機樹分類器被用作堆疊方案的最終分類器。

^{[1]}那時，我們使用帶有交叉驗證的遞歸特徵消除，它結合了直接 SVC、隨機決策森林分類器、極端隨機樹分類器、Adobos 分類器和多變量事件模型分類器作為單獨的評估器，以選擇大腦必須的豐盛亮點缺血亞組。

^{[2]}nan

^{[3]}nan

^{[4]}nan

^{[5]}實施了幾個分類器，結果比較表明，極隨機樹分類器產生最佳結果。

^{[6]}選擇了 50 個具有最高 ANOVA F 分數的特徵，並將其輸入到一個極其隨機的樹分類器中。

^{[7]}nan

^{[8]}

## randomized trees method 隨機樹法

The aim of this paper is to propose a novel prediction model based on an ensemble of deep neural networks adapting the extremely randomized trees method originally developed for random forests.^{[1]}The extremely randomized trees method provided robust performance with highest accuracy and well-balanced sensitivity and specificity (accuracy 73%, sensitivity 72%, specificity 75%, positive predictive value 33%, negative predictive value 94%, area under the curve 81%).

^{[2]}After segmentation of the remotely collected signals for gait strides identification relevant features were extracted to feed, train and test a classifier for prediction of gait abnormalities using supervised machine learning type and Extremely Randomized Trees method.

^{[3]}

本文的目的是提出一種基於深度神經網絡集合的新型預測模型，該模型採用最初為隨機森林開發的極端隨機樹方法。

^{[1]}極端隨機樹方法提供了穩健的性能，具有最高的準確性和平衡的敏感性和特異性（準確性 73%、敏感性 72%、特異性 75%、陽性預測值 33%、陰性預測值 94%、曲線下面積 81%） .

^{[2]}nan

^{[3]}

## randomized trees algorithm

In this work, we introduce a new machine learning (ML) based scoring function called ET‐Score, which employs the distance‐weighted interatomic contacts between atom type pairs of the ligand and the protein for featurizing protein−ligand complexes and Extremely Randomized Trees algorithm for the training process.^{[1]}The proposed method uses ultrasound images as input, enhanced with preprocessing techniques and segmentation for the region of interest, the segmented image is used for extracting features such as texture, shape, and size of the wounds that will be data for extremely randomized trees algorithm.

^{[2]}

在這項工作中，我們引入了一種新的基於機器學習 (ML) 的評分函數，稱為 ET-Score，它利用配體和蛋白質的原子類型對之間的距離加權原子間接觸來表徵蛋白質-配體複合物和極隨機樹算法對於訓練過程。

^{[1]}nan

^{[2]}

## randomized trees model

This matrix is used as parameters of M5 Prime, random forest, and extremely randomized trees models in order to predict the influence distances.^{[1]}In this paper, we use the physical signature of each stellar spectrum—line indices as the input features of the Extremely Randomized Trees model (ERT) to estimate the atmospheric physical parameters.

^{[2]}

該矩陣用作 M5 Prime、隨機森林和極端隨機樹模型的參數，以預測影響距離。

^{[1]}在本文中，我們使用每個恆星光譜的物理特徵——線指數作為極端隨機樹模型（ERT）的輸入特徵來估計大氣物理參數。

^{[2]}