XGBoost (eXtreme Gradient Boosting)


Notes:

Gradient boosting is a machine learning technique for regression and classification problems, which produces a prediction model in the form of an ensemble of weak prediction models, typically decision trees. It builds the model in a stage-wise fashion like other boosting methods do, and it generalizes them by allowing optimization of an arbitrary differentiable loss function.

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100 Best Decision Tree Videos100 Best h2o.ai Videos | Decision Tree & Dialog Systems 2015 | Decision Tree Classifier & Dialog SystemsDNN (Deep Neural Network) & Human Language Technology 2014


xgboost: eXtreme Gradient Boosting T Chen, T He – R package version 0.4-2, 2015 – cran.hafro.is This is an introductory document of using the xgboost package in R. xgboost is short for eXtreme Gradient Boosting package. It is an efficient and scalable implementation of gradient boosting framework by (Friedman, 2001)(Friedman et al., 2000). The package … Cited by 4 Related articles All 23 versions

Weighted classification cascades for optimizing discovery significance in the higgsml challenge L Mackey, J Bryan, MY Mo – arXiv preprint arXiv:1409.2655, 2014 – arxiv.org … The first cascade variant used the XGBoost implementation of gradient tree boosting3 to learn the base classifier gt on each round of Algorithm 1. To curb overfitting to the training set, on each cascade round, the team computed weighted true and false positive counts on a held … Cited by 4 Related articles All 8 versions

MediaEval 2014: THU-HCSIL Approach to Emotion in Music Task using Multi-level Regression. Y Fan, M Xu – MediaEval, 2014 – ceur-ws.org … Then, we define the selected features as FSLT n = {FRF n ? FET n }. Using XGBoost lib [5], the optimized n ? is deter- … Table 4 shows the results of 5 runs submitted, run 1-4 are for dynamic task and run 5 is for static task. XGBoost lib is employed for regression in all runs. … Cited by 2 Related articles All 4 versions

Higgs boson discovery with boosted trees T Chen, T He – Cowan et al., editor, JMLR: Workshop and Conference …, 2015 – jmlr.org … The algorithm is implemented as a new software package called XGBoost, which offers fast training speed and good accuracy. … The competition administrators value the potential improvement from XGBoost on the current tools used in high energy physics. … Cited by 4 Related articles All 5 versions

XGBoost: Reliable Large-scale Tree Boosting System T Chen, C Guestrin – learningsys.org Abstract Tree boosting is an important type of machine learning algorithms that is widely used in practice. In this paper, we describe XGBoost, a reliable, distributed machine learning system to scale up tree boosting algorithms. The system is optimized for fast parallel tree … Related articles

XGBoost: A Scalable Tree Boosting System T Chen, C Guestrin – arXiv preprint arXiv:1603.02754, 2016 – arxiv.org Abstract: Tree boosting is a highly effective and widely used machine learning method. In this paper, we describe a scalable end-to-end tree boosting system called XGBoost, which is used widely by data scientists to achieve state-of-the-art results on many machine …

Rossmann store sales quantity prediction V Sazontyev – pdfs.semanticscholar.org … Figure 7.Learning curve of third step. Training and test set error. x-axis – month that used on training; y-axis RMSPE error The forth step – I used random forests eXtreme gradient boosting – xgboost. This is a library that is designed, and optimized for boosted (tree) algorithms. …

Sales Forecasting for Retail Chains A Jain, MN Menon, S Chandra – cseweb.ucsd.edu … IV. EXPERIMENTS A. XGBoost: Extreme Gradient Boosting While looking at better techniques for data analysis and forecasting online, we came across XGBoost which gives much better performance results than Linear Regression or Random Forest Regression. …

Classification of Higgs Boson Tau-Tau decays using GPU accelerated Neural Networks M Shridhar – cs229.stanford.edu … 4 Experiments & Results Figure 3: Random Distribution Figure 4: Gradient Boosting Figure 5: XGBoost Figure 6: Dropout Neural Networks … 3 Page 4. Figure 7: Accuracy Benchmark Figure 8: Over-Training by the Kaggle challange participants: XGBoost & Gradient Boosting. … Related articles

Machine Learning Approach to Identify Users Across Their Digital Devices TR Anand, O Renov – 2015 IEEE International Conference on …, 2015 – ieeexplore.ieee.org … conference. The methods described in this paper focuses on feature engineering and generic machine learning algorithms like Extreme Gradient Boosting (xgboost), Follow the Reguralized Leader Proximal etc. Machine learning …

Cross-Device Consumer Identification G Kejela, C Rong – 2015 IEEE International Conference on …, 2015 – ieeexplore.ieee.org … Keywords-Ensemble; Xgboost ; Deep Learning; GBM; Ran- dom Forest; ICDM2015 contest … The same set of variables has been used for all of the models but we generated dummy variables from non-binary categorical features in case of the Xgboost model. …

Connecting Devices to Cookies via Filtering, Feature Engineering, and Boosting MS Kim, J Liu, X Wang, W Yang – 2015 IEEE International …, 2015 – ieeexplore.ieee.org … 2825-2830, (2011). [3] Chen, Tianqi, and Tong He. ”xgboost: eXtreme Gradient Boosting.” (2015). [4] Greg Ridgeway with contributions from others. gbm: Generalized Boosted Regression Models. R package version 2.1.1. http://CRAN.R- project.org/package=gbm (2015). …

Gradient Boosted Trees to Predict Store Sales M Korolev, K Ruegg – cs229.stanford.edu … We implement baseline models that are surpassed by XGBoost implemen- tation of gradient boosting trees. … Thus, this new function h(x) should be fit to predict the residual of Ft?1(x). For XGBoost, this insight is used during the derivation of the the final objective function. … Related articles

Banking failure prediction: a boosting classification tree approach A Momparler, P Carmona, F Climent – Spanish Journal of Finance …, 2016 – Taylor & Francis Related articles

Using NLP Specific Tools for Non-NLP Specific Tasks. A Web Security Application OM ?ulea, LP Dinu, A Pe?te – Neural Information Processing, 2015 – Springer … We train and test using Logistic Regression, Linear SVC [6], open-source XGBoost 1 , and Multilayer Perceptron and compare the results obtained using NLP features with those obtained using lexical and host-based features and show that the former perform similarly if not … Related articles

Exploring patterns and correlations in CMS Computing operations data with Big Data analytics techniques V Kuznetsov, T Wildish, L Giommi… – … Symposium on Grids and …, 2015 – pos.sissa.it … are adopted within DCAFPilot project: a regular set of scikit-learn classifiers [9], eg Random Forest, SGD- Classifier, SVC, etc., the online learning algorhtm, Vowpal Wabbit [10], by Yahoo, and gradient boosting tree solution (xgboost, the eXtreme Gradient Boosting) [11]. … Related articles

Facial Landmark Detection using Ensemble of Cascaded Regressions M Penev, O Boumbarov – International Journal, 2015 – ijeert.org … 1-7. [7] Chen, T. and T. He, “xgboost: eXtreme gradient boosting,” 2015. [8] Cootes, T., M. Ionita, C. Lindner, and P. Sauer, “Robust and accurate shape model fitting using random forest regression voting,” in Computer Vision – ECCV 2012. vol. … Related articles

The Higgs Machine Learning Challenge C Adam-Bourdarios, G Cowan… – Journal of Physics: …, 2015 – iopscience.iop.org … Phys. J. C 71 1554 (Preprint arXiv:1007.1727) [15] Chen T et al., XGBoost: eXtreme Gradient Boosting github.com/dmlc/xgboost [16] ATLAS Collaboration, Data set from the Higgs Machine Learning Challenge, CERN Open Data Portal opendata.cern.ch/collection/ATLAS-Higgs … Related articles All 4 versions

Involving other communities through challenges and cooperation C Nellist, ATLAS Collaboration – 2016 – cds.cern.ch … third. A HEP meets ML award was given to one team for providing XGBoost (eXtreme Gradient Boosted), a parallelised software to train boosted decision trees, which was used effectively by many of the other competitors. The …

Engineering Safety in Machine Learning KR Varshney – arXiv preprint arXiv:1601.04126, 2016 – arxiv.org … Highly complex modeling techniques used today, including extreme gradient boosting and deep neural networks, may pick up on those data vagaries in the learned models they produce to achieve high accuracy, but might fail due to an unknown shift in the data domain. … Related articles

Improving reproducibility of data science experiments T Likhomanenkoa, A Rogozhnikova, A Baranova… – indico.lal.in2p3.fr … At the moment there are wrappers over such libraries as: scikit-learn, XGBoost https: //github.com/dmlc/xgboost, TMVA, theanets https://github.com/lmjohns3/theanets, pybrain https://github.com/pybrain/pybrain, neurolab https://github.com/zueve/ neurolab. … Related articles

San Francisco Crime Classification J Ke, X Li, J Chen – cseweb.ucsd.edu … Available from: https://github.com/Lasagne/Lasagne [10] XGBoost (eXtreme Gradient Boosting): An optimized general purpose gradient boosting library (2015). Available from: https://github. com/dmlc/xgboost [11] Script on Kaggle: neural nets and address featurization (2015). … Related articles All 2 versions

Exploiting CMS data popularity to model the evolution of data management for Run-2 and beyond D Bonacorsi, T Boccali, D Giordano… – Journal of Physics: …, 2015 – iopscience.iop.org … are adopted within DCAFPilot project: a regular set of scikit-learn classifiers [15], eg Random Forest, SGDClassifier, SVC, etc., the online learning algorhtm, Vowpal Wabbit [16], by Yahoo, and gradient boosting tree solution (xgboost, the eXtreme Gradient Boosting) [17]. … Related articles All 2 versions

Multi-layer Classification: ICDM 2015 Drawbridge Cross-Device Connections Competition M Landry, R Chong – 2015 IEEE International Conference on …, 2015 – ieeexplore.ieee.org … training examples for fitting the model. We used gradient boosted machines, specifically the extreme boosting method in XGBoost, to fit our main binomial classification model at this stage. Decision tree models were very familiar …

Predict User In-World Activity via Integration of Map Query and Mobility Trace Z Wu, H Wu, T Zhang – cs.uic.edu … Here we resort to XGBoost(eXtreme Gradient Boosting), an open source gradient boosting library which also provides … [2] T. Chen and T. He. xgboost: extreme gradient boosting. 2015. [3] H. CHOI and H. VARIAN. Predicting the present with google trends. … Related articles

Machine learning: how to get more out of HEP data and the Higgs Boson Machine Learning Challenge M Wolter – XXXVI Symposium on Photonics …, 2015 – proceedings.spiedigitallibrary.org … A useful review of available implementations was presented on the 2013 NIPS workshop.28 The special award at the Higgs challenge, the “HEP meets ML” award, got the team Tianqi Chen and Tong He for providing the XGBoost public package29(XGBoost package https … Related articles

Reproducible Experiment Platform T Likhomanenko, A Rogozhnikov… – Journal of Physics: …, 2015 – iopscience.iop.org … or another hierarchical type models. REP machine learning main elements are described in Figure 2. Fundamental elements are wrappers over libraries: TMVA, scikit-learn, XGBoost [6], Event Filter, etc. Event Filter is a web … Related articles All 4 versions

Accurate prediction of rat oral acute toxicity using relevance vector machine and consensus modeling T Lei, Y Li, Y Song, D Li, H Sun… – Journal of …, 2016 – jcheminf.springeropen.com … eXtreme gradient boosting (XGBoost). Gradient boosting algorithm is a machine learning technique to construct an ensemble of decision trees, and XGBoost is an efficient and scalable implementation of the gradient boosting framework [45, 46]. …

Continuous User Authentication Using Machine Learning on Touch Dynamics ? Budulan, E Burceanu, T Rebedea, C Chiru – Neural Information …, 2015 – Springer … The final score was computed as a mean among those contained by the array. Other models (from Scikit-learn [5] or XGBoost 2 ) can be found in the final results, where all 64 features were present. … Accuracy(%). Running time (s). XGBoost. 83.60. 774. AdaBoost over DecisionTree … Related articles

Performing Highly Accurate Predictions Through Convolutional Networks for Actual Telecommunication Challenges J Zaratiegui, A Montoro, F Castanedo – arXiv preprint arXiv:1511.04906, 2015 – arxiv.org … We considered the performance of four well-known machine learning algorithms compared to the best performing WiseNet model: randomForests, generalized linear models (GLM), generalized boosted machines (GBM) and extreme gradient boosting (xgboost). … Related articles All 2 versions

Two-Stage Approach to Item Recommendation from User Sessions M Volkovs – Proceedings of the 2015 International ACM …, 2015 – dl.acm.org … We experimented with L1, L2 and dropout to prevent over-fitting and found L2 to work the best. Dropout also gave good performance but was extremely slow to con- verge. For GBM classifiers we used the excellent XGBoost 2 li- brary. … eters of XGBoost to prevent over-fitting. … Related articles All 2 versions

Matrix: sparse and dense matrix classes and methods D Bates, M Maechler – R package version 0.999375-43, URL http:// …, 2010 – cran.gis-lab.info … SID, simcausal, SimInf, sirt, smart, smint, SMNCensReg, spacom, spaMM, spatgraphs, spatstat, spatsurv, sphet, splm, stm, stocc, strum, superbiclust, surveillance, SwarmSVM, synlik, text2vec, threeboost, TMB, tmlenet, tmle.npvi, tsDyn, umx, varComp, VCA, xgboost, XMRF. … Cited by 123 Related articles All 8 versions

Predicting Sales for Rossmann Drug Stores B Knott, H Liu, A Simpson – cs229.stanford.edu … We used the R package XGBoost to train our Gradient Boosting models, then used pa- rameter optimization to find the best solution. … It has been used in several Kaggle competition-winning solutions and has been developed into a the R package XGBoost. … Related articles

RecSys Challenge 2015: ensemble learning with categorical features P Romov, E Sokolov – Proceedings of the 2015 International ACM …, 2015 – dl.acm.org … could not use common machine learning techniques that can deal with categorical features due to either their low capacity and inability to find complex interactions (eg linear classifiers) or their inability to deal with such high-dimensional datasets (eg XGBoost, Random Forest). … Related articles

Psychophysiological Sensing and State Classification for Attention Management in Commercial Aviation AR Harrivel, C Liles, CL Stephens, KK Ellis… – AIAA Infotech@ …, 2016 – arc.aiaa.org … Multi-Attribute Task Battery available at: http://matb.larc.nasa.gov/ † eXtreme Gradient Boosting (XGB) available at: https://xgboost.readthedocs.org/en/latest/ ‡ Keras available at: http://keras.io/#keras-theano-based-deep-learning-library § SKLearn available at: http://scikit- … Cited by 1

Predicting dataset popularity for the CMS experiment V Kuznetsov, T Li, L Giommi, D Bonacorsi… – arXiv preprint arXiv: …, 2016 – arxiv.org … replication. The DCAFPilot framework was implemented in python and integrated with the scikit-learn ML library [8]. We also supplement it by Yahoo Vowal Wabbit [9] and Distributed Gradient Boosting xgboost ML libraries [10]. …

Novel feature extraction, selection and fusion for effective malware family classification M Ahmadi, G Giacinto, D Ulyanov, S Semenov… – arXiv preprint arXiv: …, 2015 – arxiv.org … On the other hand, most of the winners in the very recent Kaggle competitions used the XGBoost technique [8], which is a parallel implementation of the gra- dient boosting tree classifier, that in most of the cases pro- duced better performances than those produced by random … Cited by 1 Related articles All 2 versions

Modern Models for Learning Large-Scale Highly Skewed Online Advertising Data Q Zhang – 2015 – escholarship.org … A graduate student at University of Washington introduced an optimized general purpose gradient boosting library under Apache license called xgboost which takes in sparse data … ficiently. Xgboost receives wide recognition among machine learning community … Related articles All 5 versions

Report from THU_ML_PHYS Y SONG, Z ZHANG, J LI – bigml.cs.tsinghua.edu.cn … For GBDT(Gradient Boosting Decision Tree), we used the R language and R package xgboost to do it. Xgboost[21] is “an optimized general purpose gradient boosting library. … For more information you can refer to xgboost’s github page[21]. Data Cleaning … Related articles All 2 versions

Predicting customer loyalty J Raats, L Van der Zwan, M Larson, H Hung, C Broeren… – 2015 – repository.tudelft.nl … We decided to use an online learner package called Vowpal Wabbit and a booster package called xgboost. … Vowpal wabbit produces a list with the percentages of relevance per feature, whereas xgboost just produces a list of the most relevant ones. … Related articles All 2 versions

Predicting customer loyalty J Raats, L Van der Zwan, M Larson, H Hung, C Broeren… – 2015 – repository.tudelft.nl … We decided to use an online learner package called Vowpal Wabbit and a booster package called xgboost. … Vowpal wabbit produces a list with the percentages of relevance per feature, whereas xgboost just produces a list of the most relevant ones. … Related articles All 2 versions

Why is My Question Closed? Predicting and Visualizing Question Status on Stack Overflow Y Lao, C Xie, Y Wang – pdfs.semanticscholar.org … user-good posts: number of good posts the use receive at the posting time of this post 37. user-reputation: user’s reputation at the posting time of this post 3.3. Experiment and evaluation We use XGBoost’s [3] implementation of gradient boosted classification tree. …

The Higgs boson machine learning challenge C Adam-Bourdarios, G Cowan, C Germain… – NIPS 2014 Workshop …, 2014 – hal.inria.fr Page 1. The Higgs boson machine learning challenge Claire Adam-Bourdarios, Glen Cowan, Cécile Germain, Isabelle Guyon, Balázs Kégl, David Rousseau To cite this version: Claire Adam-Bourdarios, Glen Cowan, Cécile Germain, Isabelle Guyon, Balázs Kégl, et al.. … Related articles All 9 versions

A new boosting algorithm based on dual averaging scheme N Wang – arXiv preprint arXiv:1507.03125, 2015 – arxiv.org … (1999), a machine learning method that is famous for its resistance to over-fitting. For example, the winners of the HiggsML Challenge on Kaggle, develop and use the Boosting library, XGBoost Chen et al. (2013), to win this competition. … Cited by 1 Related articles All 3 versions

Reusing ML tools and approaches for HEP A Ustyuzhanin – 2015 – cds.cern.ch … Andrey Ustyuzhanin 9 Page 10. Technology part Reproducible Experiment Platform (REP) machine learning toolbox for humans › unified classifier wrappers for › Sklearn, XGBoost, uBoost, TMVA, ANN, Theanets, PyBrain, … › pluggable quality metrics › support for interactive …

Rossmann Store Sales D Beam, M Schramm – 2015 – pdfs.semanticscholar.org … distance. 2) Introduction to Boosted Trees – Tianqi Chen University of Washington We used gradient boost- ing because many competitions used and endorsed xgboost, a python library for decision-tree based gradient boosting. …

Scikit-Learn in particle physics G Louppe – Data Science Academic software: From scikit-learn and …, 2014 – orbi.ulg.ac.be … Winning methods • Ensembles of neural networks (1st and 3rd) ; • Ensembles of regularized greedy forests (2nd) ; • Boosting with regularization (XGBoost package). • Most contestants dit not optimize AMS directly ; • But chosed the prediction cut-off maximizing AMS in CV. 7 / 13 … All 5 versions

Package ‘FeatureHashing’ W Wu, M Benesty – 2015 – cran.utstat.utoronto.ca … Depends R (>= 3.1), methods Imports Rcpp (>= 0.11), Matrix, digest(>= 0.6.8), magrittr (>= 1.5) LinkingTo Rcpp, digest(>= 0.6.8), BH Suggests RUnit, glmnet, knitr, xgboost, rmarkdown SystemRequirements C++11 BugReports https://github.com/wush978/FeatureHashing/issues … All 70 versions

Evasion and Hardening of Tree Ensemble Classifiers A Kantchelian, JD Tygar, AD Joseph – arXiv preprint arXiv:1509.07892, 2015 – arxiv.org … We choose to use second order gradient boosting (denoted BDT) in the XGBoost [15] implementation as the tree en- semble learner because of its outstanding performance as an off-the-shelf general purpose learning algorithm. … XGBoost: eXtreme Gradient Boosting. … Related articles All 2 versions

Collaborative Embedding Features and Diversified Ensemble for E-Commerce Repeat Buyer Prediction Z Fang, Z Yang, Y Zhang – kimiyoung.github.io … Morgan Kaufmann Publishers Inc., 1998. [Chen and He, 2015] Tianqi Chen and Tong He. xgboost: extreme gradient boosting. 2015. [Cox, 1958] David R Cox. The regression analysis of binary sequences. Journal of the Royal Statistical Society. … Related articles All 3 versions

Lowering the volatility: a practical cache allocation prediction and stability-oriented co-runner scheduling algorithms F Wang, X Gao, G Chen – The Journal of Supercomputing, 2016 – Springer Page 1. J Supercomput DOI 10.1007/s11227-016-1645-7 Lowering the volatility: a practical cache allocation prediction and stability-oriented co-runner scheduling algorithms Fei Wang1 · Xiaofeng Gao1 · Guihai Chen1 © Springer Science+Business Media New York 2016 …

Beyond Ranking: Optimizing Whole-Page Presentation Y Wang, D Yin, L Jie, P Wang, M Yamada… – Proceedings of the …, 2016 – dl.acm.org Page 1. Beyond Ranking: Optimizing Whole-Page Presentation Yue Wang1 ? , Dawei Yin2, Luo Jie3 † , Pengyuan Wang2, Makoto Yamada2,4, Yi Chang2, Qiaozhu Mei1,5 1Department of EECS, University of Michigan, Ann …

Optimization of AMS using Weighted AUC optimized models R D?az-Morales, A Navia-Vázquez – JMLR W&CP, 2015 – jmlr.csail.mit.edu … Gradient Boosting Machines: Boosting techniques build models using the information of weak predictors, typically decision trees. The software that we used was XGBoost (Chen, 2014). … URL https: //github.com/tqchen/xgboost. … Cited by 2 Related articles All 6 versions

Predicting effects of noncoding variants with deep learning-based sequence model J Zhou, OG Troyanskaya – Nature methods, 2015 – nature.com DeepSEA, a deep-learning algorithm trained on large-scale chromatin-profiling data, predicts chromatin effects from sequence alone, has single-nucleotide sensitivity and can predict effects of noncoding variants. Cited by 8 Related articles All 4 versions

Automated Parameter Optimization of Classification Techniques for Defect Prediction Models C Tantithamthavorn, S McIntosh, AE Hassan… – … (ICSE), page To …, 2016 – chakkrit.com … Techniques: Gradient Boosting Machine (GBM), Adap- tive Boosting (AdaBoost), Generalized linear and Addi- tive Models Boosting (GAMBoost), Logistic Regression Boosting (LogitBoost), eXtreme Gradient Boosting Tree (xGBTree), and C5.0. … Cited by 1

Exploring the Power of Frequent Neighborhood Patterns on Edge Weight Estimation L Xiong – 2015 – summit.sfu.ca Page 1. EXPLORING THE POWER OF FREQUENT NEIGHBORHOOD PATTERNS ON EDGE WEIGHT ESTIMATION by Li Xiong B.Eng., Sichuan University, 2013 a Thesis submitted in partial fulfillment of the requirements for the degree of Master of Science in the … Related articles

Real-time bidding benchmarking with iPinYou dataset W Zhang, S Yuan, J Wang, X Shen – arXiv preprint arXiv:1407.7073, 2014 – arxiv.org Page 1. Real-Time Bidding Benchmarking with iPinYou Dataset Weinan Zhang, Shuai Yuan, Jun Wang University College London {w.zhang, s.yuan, j.wang}@cs.ucl.ac. uk Xuehua Shen iPinYou Inc. x@ipinyou.com ABSTRACT … Cited by 7 Related articles All 2 versions

Cross-Device Tracking: Matching Devices and Cookies R Díaz-Morales – arXiv preprint arXiv:1510.01175, 2015 – arxiv.org … The soft- ware that we used was XGBoost [15], an open source C++ implementation that utilizes OpenMP to perform automatic parallel computation on a multi-threaded CPU to speedup the training procedure. It has proven its efficiency in many challenges [16][17][18]. … Related articles All 3 versions

Cross-Device Tracking: Matching Devices and Cookies R D?az-Moralesl – ieeexplore.ieee.org … The soft- ware that we used was XGBoost [15], an open source C++ implementation that utilizes OpenMP to perform automatic parallel computation on a multi-threaded CPU to speedup the training procedure. It has proven its efficiency in many challenges [16][17][18]. …

Introduction to Boosted Trees T Chen – … of Washing Computer Science. University of …, 2014 – homes.cs.washington.edu … R. Johnson and T. Zhang ? Proposes to do fully corrective step, as well as regularizing the tree complexity. The regularizing trick is closed related to the view present in this slide • Software implementing the model described in this slide: https://github.com/tqchen/xgboost Cited by 3 Related articles All 7 versions

WaterlooClarke: TREC 2015 Total Recall Track H Zhang, W Lin, Y Wang, CLA Clarke, MD Smucker – pdfs.semanticscholar.org … SVM & LR fusion Sofia-ML 4-gram TF-IDF RBF SVM LIBSVM Entropy RBF SVM LIBSVM Unigram TF-IDF Decision Tree Scikit-Learn Unigram TF-IDF Naive Bayes Scikit-Learn Unigram TF-IDF AdaBoost Scikit-Learn Unigram TF-IDF Gradient Boosting XGboost Unigram TF-IDF …

Misleading Metrics: On Evaluating Machine Learning for Malware with Confidence R Jordaney, Z Wang, D Papini, I Nouretdinov… – royalholloway.ac.uk … The algorithm is described in [2]. The authors use the eX- treme Gradient Boosting (XGBoost) as their machine learning classification algorithm [26]. It’s … one. Particularly in their work, the authors use XGBoost with decision trees. …

Developing an analytics-driven sales process: a case study in the field of corporate banking M Wallin – 2015 – aaltodoc.aalto.fi Page 1. Aalto University School of Science Degree Programme in Industrial Engineering and Management Michael Wallin Developing an analytics-driven sales process: a case study in the field of corporate banking Master’s Thesis Espoo, 24.6.2015 … Related articles