## What is/are Randomized Trees?

Randomized Trees - 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.^{[1]}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.

^{[2]}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.

^{[3]}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.

^{[4]}

## support vector machine

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]}

## 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]}

## multi layer perceptron

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]}

## Extremely Randomized Trees

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]}

## 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]}

## randomized trees classifier

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]}

## randomized trees method

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]}

## 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]}

## 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]}