xgBoost

Things are sold at a substantial discount on the eve of Black Friday, resulting in sales that are 30 times larger than on normal flash sale days. Customers' data from purchases made on this day can be examined, resulting in a quick declaration of their preferences for specific products. We looked at data that contained packets of clients, and also the factors that influenced their purchases and the amounts they spent. This data is analysed and forecasted purely for the purpose of providing clients with customized discounts on goods depending on individual preferences and purchase budget. Four models were employed to forecast significant variations in training and test data (50:50, 70:30, 30:70), as well as a distinct sample training and testing dataset with two additional examples of prediction: xgboost, tfidftransform, both combination, and extra trees regressor. The two scenarios involve forecasting and analysing another dataset, as well as projected on the train data and testing data on a different testing data set. The dataset would be analyzed to learn about consumer behavior and trends of product the sale's popularity. For each of the five scenarios, the feature significance and benefit importance are displayed. All of the models' accuracy in various settings has been given in the manner of accuracy graphs and the accuracy findings have been displayed in the form of an RMSE score.

Gradient Boosting Decision Trees (GBDT) algorithms have been proven to be among the best algorithms in machine learning. XGBoost, the most popular GBDT algorithm, has won many competitions on websites like Kaggle. However, XGBoost is not the only GBDT algorithm with state-of-the-art performance. There are other GBDT algorithms that have more advantages than XGBoost and sometimes even more potent like LightGBM and CatBoost. This paper aims to compare the performance of CPU implementation of the top three gradient boosting algorithms. We start by explaining how the three algorithms work and the hyperparameters similarities between them. Then we use a variety of performance criteria to evaluate their performance. We divide the performance criteria into four: accuracy, speed, reliability, and ease of use. The performance of the three algorithms has been tested with five classification and regression problems. Our findings show that the LightGBM algorithm has the best performance of the ...

Company bankruptcy is often a very big problem for companies. The impact of bankruptcy can cause losses to elements of the company such as owners, investors, employees, and consumers. One way to prevent bankruptcy is to predict the possibility of bankruptcy based on the company's financial data. Therefore, this study aims to find the best predictive model or method to predict company bankruptcy using the dataset from Polish companies bankruptcy. The prediction analysis process uses the best feature selection and ensemble learning. The best feature selection is selected using feature importance to XGBoost with a weight value filter of 10. The ensemble learning method used is stacking. Stacking is composed of the base model and meta learner. The base model consists of K-nearest neighbor, decision tree, support vector machines (SVM), and random forest, while the meta learner used is LightGBM. The stacking model accuracy results can outperform the base model accuracy with an accuracy rate of 97%.

Several credit-scoring models have been developed using ensemble classifiers in order to improve the accuracy of assessment. However, among the ensemble models, little consideration has been focused on the hyperparameters tuning of base learners, although these are crucial to constructing ensemble models. This study proposes an improved credit scoring model based on the extreme gradient boosting (XGB) classifier using Bayesian hyper-parameters optimization (XGB-BO). The model comprises two steps. Firstly, data pre-processing is utilized to handle missing values and scale the data. Secondly, Bayesian hyper-parameter optimization is applied to tune the hyper-parameters of the XGB classifier and used to train the model. The model is evaluated on four widely public datasets, i.e., the German, Australia, Lending club, and Polish datasets. Several state-of-the-art classification algorithms are implemented for predictive comparison with the proposed method. The results of the proposed model showed promising results, with an improvement in accuracy of 4.10%, 3.03%, and 2.76% on the German, Lending club, and Australian datasets, respectively. The proposed model outperformed commonly used techniques, e.g., decision tree, support vector machine, neural network, logistic regression, random forest, and bagging, according to the evaluation results. The experimental results confirmed that the XGB-BO model is suitable for assessing the creditworthiness of applicants.

Our society is getting more and more technology dependent day by day. Nevertheless, agriculture is imperative for our survival. Rice is one of the primary food grains. It provides sustenance to almost fifty percent of the world population and promotes huge amount of employments. Hence, proper mitigation of rice plant diseases is of paramount importance. A model to detect three rice leaf diseases, namely bacterial leaf blight, brown spot, and leaf smut is proposed in this paper. Backgrounds of the images are removed by saturation threshold while disease affected areas are segmented using hue threshold. Distinctive features from color, shape, and texture domain are extracted from affected areas. These features can robustly describe local and global statistics of such images. Trying a couple of classification algorithms, extreme gradient boosting decision tree ensemble is incorporated in this model for its superior performance. Our model achieves 86.58% accuracy on rice leaf diseases dataset from UCI, which is higher than previous works on the same dataset. Class-wise accuracy of the model is also consistent among the classes.

In the cyber era, Machine Learning (ML) has provided us with the solutions to these problems with the implementation of Gradient Boosting Machines (GBM). We have ample algorithms to choose from to do gradient boosting for our training data but still, we encounter different issues like poor accuracy, high loss, large variance in the result. Here, we are going to introduce you to a state of the art machine learning algorithm XGBoost built by Tianqi Chen, that will not only overcome the issues but also perform exceptionally well for regression and classification problems. This blog will help you discover the insights, techniques, and skills with XGBoost that you can then bring to your machine learning projects.
eXtreme Gradient Boosting (XGBoost) is a scalable and improved version of the gradient boosting algorithm (terminology alert) designed for efficacy, computational speed and model performance. It is an open-source library and a part of the Distributed Machine Learning Community. XGBoost is a perfect blend of software and hardware capabilities designed to enhance existing boosting techniques with accuracy in the shortest amount of time. Here’s a quick look at an objective benchmark comparison of XGBoost with other gradient boosting algorithms trained on random forest with 500 trees, performed by Szilard Pafka.

With the recent boosted enthusiasm in Artificial Intelligence (AI) and Financial Technology (FinTech), applications such as credit scoring have gained substantial academic interest. However, despite the evergrowing achievements, the biggest obstacle in most AI systems is their lack of interpretability. This deficiency of transparency limits their application in different domains including credit scoring. Credit scoring systems help financial experts make better decisions regarding whether or not to accept a loan application so that loans with a high probability of default are not accepted. Apart from the noisy and highly imbalanced data challenges faced by such credit scoring models, recent regulations such as the `right to explanation' introduced by the General Data Protection Regulation (GDPR) and the Equal Credit Opportunity Act (ECOA) have added the need for model interpretability to ensure that algorithmic decisions are understandable and coherent. A recently introduced concept is eXplainable AI (XAI), which focuses on making black-box models more interpretable. In this work, we present a credit scoring model that is both accurate and interpretable. For classification, state-of-the-art performance on the Home Equity Line of Credit (HELOC) and Lending Club (LC) Datasets is achieved using the Extreme Gradient Boosting (XGBoost) model. The model is then further enhanced with a 360-degree explanation framework, which provides different explanations (i.e. global, local feature-based and local instance- based) that are required by different people in different situations. Evaluation through the use of functionally-grounded, application-grounded and human-grounded analysis shows that the explanations provided are simple and consistent as well as correct, effective, easy to understand, sufficiently detailed and trustworthy.

With the recent boosted enthusiasm in Artificial Intelligence (AI) and Financial Technology (FinTech), applications such as credit scoring have gained substantial academic interest. However, despite the evergrowing achievements, the biggest obstacle in most AI systems is their lack of interpretability. This deficiency of transparency limits their application in different domains including credit scoring. Credit scoring systems help financial experts make better decisions regarding whether or not to accept a loan application so that loans with a high probability of default are not accepted. Apart from the noisy and highly imbalanced data challenges faced by such credit scoring models, recent regulations such as the `right to explanation' introduced by the General Data Protection Regulation (GDPR) and the Equal Credit Opportunity Act (ECOA) have added the need for model interpretability to ensure that algorithmic decisions are understandable and coherent. A recently introduced con...

Gradient Boosting Decision Trees (GBDT) algorithms have been proven to be among the best algorithms in machine learning. XGBoost, the most popular GBDT algorithm, has won many competitions on websites like Kaggle. However, XGBoost is not the only GBDT algorithm with state-of-the-art performance. There are other GBDT algorithms that have more advantages than XGBoost and sometimes even more potent like LightGBM and CatBoost. This paper aims to compare the performance of CPU implementation of the top three gradient boosting algorithms. We start by explaining how the three algorithms work and the hyperparameters similarities between them. Then we use a variety of performance criteria to evaluate their performance. We divide the performance criteria into four: accuracy, speed, reliability, and ease of use. The performance of the three algorithms has been tested with five classification and regression problems. Our findings show that the LightGBM algorithm has the best performance of the three with a balanced combination of accuracy, speed, reliability, and ease of use, followed by XGBoost with the histogram method, and CatBoost came last with slow and inconsistent performance.

Kepolisian Republik Indonesia menyebutkan kecelakaan lalu lintas masih menjadi salah satu penyumbang angka kematian yang cukup tinggi, sudah menjadi masalah sosial yang dianggap ‘normal’ bagi masyarakat Indonesia karena intensitasnya yang semakin tinggi. Hal ini tentu harus menjadi perhatian bersama dan mendapatkan penanganan yang efektif. Polresta Denpasar mencatat setiap kejadian kecelakaan lalu lintas yang terjadi. Dari data yang terkumpul itu dapat digali informasi yang dapat digunakan untuk menanggulangi, mencegah dan mengurangi angka kecelakaan lalu lintas. Banyaknya jumlah kecelakaan yang terjadi, menimbulkan adanya permasalahan bagaimana cara memberikan informasi kepada pemerintah dan pihak terkait mengenai jumlah kecelakaan yang terjadi pada tahun berikutnya, sehingga pihak pemerintah dan pihak yang terkait dapat lebih serius dalam mengatasi kasus kecelakaan di tahun-tahun berikutnya. Salah satu cara untuk mendapatkan informasi berguna yang belum diketahui sebelumnya adalah dengan data mining. Pada penelitian ini metode yang digunakan adalah klasifikasi menggunakan xgboost dan random forest dengan parameter berdasarkan variabel yang terdapat pada data.

In this paper is designed and realized a 3D web configurator platform able to optimize the creation of 3D objects for sale on multi e-commerce platform. The user can select online the preferred object by learning the configurator to optimize the 3D object features. A Support Vector Machine-SVM-algorithm adopting XGBoost approach, improved affect computing, user navigation experience and Visual Merchandising-VM-approach, all elements which support the creation of new object having the best effective features. The intelligent self-learning platform is designed for the real time construction of 3D CAD objects and integrates different user facilities such as augmented reality, web plugin, and WebGL graphical libraries. The proposed work follows the research and development-R&D-guidelines of 'Frascati' manual concerning new search engine as research topic and summarize the result of an industry research project.

The world is intrigued by data. In fact, huge capitals are invested to devise means that implements statistics and extract analytics from these sources. However, when we examine the studies performed on applicant tracking systems that retrieve valuable information from candidates’ CVs and job-descriptions, they are mostly rule-based and hardly manage to employ contemporary techniques. Even though these documents vary in contents: the structure is almost identical. Accordingly, in this paper, we implement an NLP pipeline for the extraction of such structured information from a wide variety of textual documents. As a reference, textual documents which are used in applicant tracking systems like CV (Curriculum Vitae) and job vacancy information have been considered. The proposed NLP pipeline is built with several NLP techniques like document classification, document segmentation and text extraction. Initially for the classification of textual documents, Support Vector Machines (SVM) and XGBoost algorithms have been implemented. Different segments of the identified document are categorized using NLP techniques such as chunking, regex matching and POS tagging. Relevant information from every segment is further extracted using techniques like Named Entity Recognition (NER), regex matching and pool parsing. Extraction of such structured information from textual documents can help to gain insights and use those insights in document maintenance, document scoring, matching and auto filling forms.

In this paper is designed and realized a 3D web configurator platform able to optimize the creation of 3D objects for sale on multi e-commerce platform. The user can select online the preferred object by learning the configurator to optimize the 3D object features. A Support Vector Machine -SVMalgorithm adopting XGBoost approach, improved affect computing, user navigation experience and Visual Merchandising – VMapproach, all elements which support the creation of new object having the best effective features. The intelligent self-learning platform is designed for the real time construction of 3D CAD objects and integrates different user facilities such as augmented reality, web plugin, and WebGL graphical libraries. The proposed work follows the research and development -R&Dguidelines of ‘Frascati’ manual concerning new search engine as research topic and summarize the result of an industry research project.

Since its introduction, television has been the main channel of investment for advertisements in order to influence customers purchase behavior. Many have attributed the mere exposure effect as the source of influence in purchase intention and purchase decision; however, most of the studies of television advertisement effects are not only outdated, but their sample size is ques- tionable and their environments do not reflect reality. With the advent of the internet, social media and new information technologies, many recent studies focus on the effects of online advertisement, meanwhile the investment in television advertisement still has not declined. In response to this, we applied machine learning algorithms SVM and XGBoost, as well as Logistic Regression, to construct a number of prediction models based on at-home advertisement ex- posure time and demographic data, examining the predictability of Actual Purchase and Purchase Intention behaviors of 3000 customers across 36 different products during the span of 3 months. If we were able to predict purchase behaviors with models based on exposure time more reliably than with models based on demographic data, the obvious strategy for businesses would be to increase the number of adverts. On the other hand, if models based on exposure time had un- reliable predictability in contrast to models based on demographic data, doubts would surface about the effectiveness of the hard investment in television advertising. Based on our results, we found that models based on advert exposure time were consistently low in their predictability in comparison with models based on demographic data only, and with models based on both de- mographic data and exposure time data. We also found that there was not a statistically sig- nificant difference between these last two kinds of models. This suggests that advert exposure time has little to no effect in the short-term in increasing positive actual purchase behavior.

The world is intrigued by data. In fact, huge capitals are invested to devise means that implements statistics and extract analytics from these sources. However, when we examine the studies performed on applicant tracking systems that retrieve valuable information from candidates’ CVs and job-descriptions, they are mostly rule-based and hardly manage to employ contemporary techniques. Even though these documents vary in contents: the structure is almost identical. Accordingly, in this paper, we implement an NLP pipeline for the extraction of such structured information from a wide variety of textual documents. As a reference, textual documents which are used in applicant tracking systems like CV (Curriculum Vitae) and job vacancy information have been considered. The proposed NLP pipeline is built with several NLP techniques like document classification, document segmentation and text extraction. Initially for the classification of textual documents, Support Vector Machines (SVM) and XGBoost algorithms have been implemented. Different segments of the identified document are categorized using NLP techniques such as chunking, regex matching and POS tagging. Relevant information from every segment is further extracted using techniques like Named Entity Recognition (NER), regex matching and pool parsing. Extraction of such structured information from textual documents can help to gain insights and use those insights in document maintenance, document scoring, matching and auto filling forms.

Classification network traffic are becoming ever more relevant in understanding and addressing security issues in Internet applications. Virtual Private Networks (VPNs) have become one famous communication forms on the Internet. In this study, a new model for traffic classification into VPN or non-VPN is proposed. XGBoost algorithm is used to rank features and to build the classification model. The proposed model overwhelmed other classification algorithms. The proposed model achieved 91.6% accuracy which is the highest registered accuracy for the selected dataset. To illustrate the merit of the proposed model, a comparison was made with sixteen different classification algorithms.

The recent years have witnessed an upsurge in the number of published documents. Organizations are showing an increased interest in text classification for effective use of the information. Manual procedures for text classification can be fruitful for a handful of documents, but the same lack in credibility when the number of documents increases besides being laborious and time-consuming. Text mining techniques facilitate assigning text strings to categories rendering the process of classification fast, accurate, and hence reliable. This paper classifies chemistry documents using machine learning and statistical methods. The procedure of text classification has been described in chronological order like data preparation followed by processing, transformation, and application of classification techniques culminating in the validation of the results.

Reducing the risk pose by phishers and other cybercriminals in the cyber space requires a robust and automatic means of detecting phishing websites, since the culprits are constantly coming up with new techniques of achieving their goals almost on daily basis. Phishers are constantly evolving the methods they used for luring user to revealing their sensitive information. Many methods have been proposed in past for phishing detection. But the quest for better solution is still on. This research covers the development of phishing website model based on different algorithms with different set of features in order to investigate the most significant features in the dataset.

This research paper demonstrates the invention of the kinetic bands, based on Romanian mathematician and statistician Octav Onicescu's kinetic energy, also known as "informational energy", where we use historical data of foreign exchange currencies or indexes to predict the trend displayed by a stock or an index and whether it will go up or down in the future. Here, we explore the imperfections of the Bollinger Bands to determine a more sophisticated triplet of indicators that predict the future movement of prices in the Stock Market. An Extreme Gradient Boosting Modelling was conducted in Python using historical data set from Kaggle, the historical data set spanning all current 500 companies listed. An invariable importance feature was plotted. The results displayed that Kinetic Bands, derived from (KE) are very influential as features or technical indicators of stock market trends. Furthermore, experiments done through this invention provide tangible evidence of the empirical aspects of it. The machine learning code has low chances of error if all the proper procedures and coding are in play. The experiment samples are attached to this study for future references or scrutiny.

With the ever-growing achievements in Artificial Intelligence (AI) and the recent boosted enthusiasm in Financial Technology (FinTech), applications such as credit scoring have gained substantial academic interest. Credit scoring helps financial experts make better decisions regarding whether or not to accept a loan application, such that loans with a high probability of default are not accepted. Apart from the noisy and highly imbalanced data challenges faced by such credit scoring models, recent regulations such as the `right to explanation' introduced by the General Data Protection Regulation (GDPR) and the Equal Credit Opportunity Act (ECOA) have added the need for model interpretability to ensure that algorithmic decisions are understandable and coherent. An interesting concept that has been recently introduced is eXplainable AI (XAI), which focuses on making black-box models more interpretable. In this work, we present a credit scoring model that is both accurate and inter...

This research paper demonstrates the invention of the kinetic bands, based on Romanian mathematician and statistician Octav Onicescu's kinetic energy, also known as "informational energy", where we use historical data of foreign exchange currencies or indexes to predict the trend displayed by a stock or an index and whether it will go up or down in the future. Here, we explore the imperfections of the Bollinger Bands to determine a more sophisticated triplet of indicators that predict the future movement of prices in the Stock Market. An Extreme Gradient Boosting Modelling was conducted in Python using historical data set from Kaggle, the historical data set spanning all current 500 companies listed. An invariable importance feature was plotted. The results displayed that Kinetic Bands, derived from (KE) are very influential as features or technical indicators of stock market trends. Furthermore, experiments done through this invention provide tangible evidence of the ...

This study evaluates and compares three machine learning models: K-Nearest Neighbor (KNN), Random Forest (RF), and Extreme Gradient Boosting (XGBoost) for landslide susceptibility mapping for part of areas in Rangamati District, Bangladesh. The performance of these methods has been assessed by employing statistical methods such as the area under the curve (AUC) for success rate (SR) and prediction rate (PR), Kappa index, Qs index, and Friedman's test. Results show that XGBoost had the best performance with the highest AUC for both SR (95.27%) and PR (90.63%), followed by RF (SR: 89.26%; PR: 84.74%) and KNN models (SR: 85.54%; PR: 81.02%). This study provides a useful analysis for the selection of the best model for landslide susceptibility mapping and that it will be helpful for disaster planning and risk reduction.

Healthcare or health insurance agencies have been in a continuous state of change, especially during the present technology expansion. They are beneath colossal pressure to forecast customer health issue and to generate surplus premium holders which will simultaneously reduce the cost. Examining and utilizing the vast data available are critical for healthcare companies in designing various strategies in the future. Many such healthcare organizations have already moved toward data mining and analytics with data warehouse methodologies and business intelligence with statistical analysis. However, further adaptation is required, as they must use different data from new sources in a blend with the prior sources. To consider this adaptation, predictive analysis technique is proposed. Predictive analysis comprises diverse statistical methods from predictive modeling, machine learning, and data mining that analyze present and past realities to make predictions about future. There are several advantages of using descriptive and predictive analytics in healthcare domain for concrete decision making of cost-effective solutions to their customers. This paper expands upon risk mitigation tactics to foresee high-risk patients. This is done by considering clinical data that is an electronic medical record and evaluating risk associated using stacked ensemble machine learning techniques. This technique helps achieve higher predictive accuracy of 90.17% and specificity of 94.90% in identifying high-risk patients from a lesser amount of data. It has been centered on payer analytics and customer analytics in light of novel machine learning algorithm to give full-cycle knowledge toward cost reduction and improvement in nature of care.