XGBoost stands for eXtreme Gradient Boosted trees. It is an ensemble machine learning method. In this type of learning, the base weak learners, working in a chained sequence, learn from each other's mistakes and try to achieve good results with small improvements. Ensemble learning algorithms are super powerful and XGBoost is the superstar. It is widely used because of its ease of use, speed, and achievement. It works for both regression and classification problems and there's a really good chance that it is going to prove to be the best model to fit your data.

The most common gynecological disorder affecting women globally is known as polycystic ovary syndrome (PCOS). The symptoms of PCOS include irregular periods, hirsutism, thinning hair and hair loss over head, oily skin or acne and weight gain. PCOS can lead to risk in later life with a lifelong situation that causes a person's blood sugar levels to promote type-II diabetes. High blood pressure and high cholesterol which can lead to heart stroke, overweight ladies may expand sleep apnoea, a situation that causes interrupted breathing at some stage in sleep. Around 10 - 15% of reproductive age (15 to 49 years) of women suffer from this. The monetary expenses of this disease and its comorbidities need the development of instruments and techniques so one can permit for early and precise identification. To cope with this problem this paper proposes a system for the early detection and prediction of PCOS from the most reliable and minimal and promising scientific and metabolic parameters, which is early detection for these diseases. Machine Learning[Shinde and Shah, 2018] can be leveraged to perform prognostication of PCOS that exigently extracts factual records from the given statistics considering the fact that machine learning is better known as glorified statistics. A specific type of machine learning algorithm that seeks to improve the overall performance by combining the predictions from more than one model which is a trendy meta method is known as an Ensemble Learning Approach.

In this work we evaluate the applicability of an ensemble of population models and machine learning models to predict the near future evolution of the COVID-19 pandemic, with a particular use case in Spain. We rely solely in open and public datasets, fusing incidence, vaccination, human mobility and weather data to feed our machine learning models (Random Forest, Gradient Boosting, k-Nearest Neighbours and Kernel Ridge Regression). We use the incidence data to adjust classic population models (Gompertz, Logistic, Richards, Bertalanffy) in order to be able to better capture the trend of the data. We then ensemble these two families of models in order to obtain a more robust and accurate prediction. Furthermore, we have observed an improvement in the predictions obtained with machine learning models as we add new features (vaccines, mobility, climatic conditions), analyzing the importance of each of them using Shapley Additive Explanation values. As in any other modelling work, data and predictions quality have several limitations and therefore they must be seen from a critical standpoint, as we discuss in the text. Our work concludes that the ensemble use of these models improves the individual predictions (using only machine learning models or only population models) and can be applied, with caution, in cases when compartmental models cannot be utilized due to the lack of relevant data.

Energy has been propelling the development of human civilization for millennia, and technologies acquiring energy beyond human and animal power have been continuously advanced and transformed. In 1964, the Kardashev Scale was proposed to quantify the relationship between energy consumption and the development of civilizations. Human civilization presently stands at Type 0.7276 on this scale. Projecting the future energy consumption, estimating the change of its constituting structure, and evaluating the influence of possible technological revolutions are critical in the context of civilization development. In this study, we use two machine learning models, random forest (RF) and autoregressive integrated moving average (ARIMA), to simulate and predict energy consumption on a global scale. We further project the position of human civilization on the Kardashev Scale in 2060. The result shows that the global energy consumption is expected to reach 928-940 EJ in 2060, with a total growth of over 50% in the coming 40 years, and our civilization is expected to achieve Type 0.7474 on the Kardashev Scale, still far away from a Type 1 civilization. Additionally, we discuss the potential energy segmentation change before 2060 and present the influence of the advent of nuclear fusion in this context.

Structural magnetic resonance imaging studies have shown that brain anatomical abnormalities are associated with cognitive deficits in preterm infants. Brain maturation and geometric features can be used with machine learning models for predicting later neurodevelopmental deficits. However, traditional machine learning models would suffer from a large feature-to-instance ratio (i.e., a large number of features but a small number of instances/samples). Ensemble learning is a paradigm that strategically generates and integrates a library of machine learning classifiers and has been successfully used on a wide variety of predictive modeling problems to boost model performance. Attribute (i.e., feature) bagging method is the most commonly used feature partitioning scheme, which randomly and repeatedly draws feature subsets from the entire feature set. Although attribute bagging method can effectively reduce feature dimensionality to handle the large feature-to-instance ratio, it lacks consideration of domain knowledge and latent relationship among features. In this study, we proposed a novel Ontology-guided Attribute Partitioning (OAP) method to better draw feature subsets by considering the domain-specific relationship among features. With the better partitioned feature subsets, we developed an ensemble learning framework, which is referred to as OAP-Ensemble Learning (OAP-EL). We applied the OAP-EL to predict cognitive deficits at 2 years of age using quantitative brain maturation and geometric features obtained at term equivalent age in very preterm infants. We demonstrated that the proposed OAP-EL approach significantly outperformed the peer ensemble learning and traditional machine learning approaches.

Adaptive Boosting Classifier is an ensemble classifier developed by Yoav Freund and Robert Schapire. This algorithm works by creating a prediction model in the form of a set of weak models. It requires specifying a set of weak learners before actually starting it. The weight of each model is determined based on whether it correctly predicted the sample or not. In a situation where the learner has predicted wrong, his weight is slightly reduced. The whole process is carried out until convergence[1].

Estimating the importance of variables is an essential task in modern machine learning. This help to evaluate the goodness of a feature in a given model. Several techniques for estimating the importance of variables have been developed during the last decade. In this paper, we proposed a computational and theoretical exploration of the emerging methods of variable importance estimation, namely: Least Absolute Shrinkage and Selection Operator (LASSO), Support Vector Machine (SVM), the Predictive Error Function (PERF), Random Forest (RF), and Extreme Gradient Boosting (XGBOOST) that were tested on different kinds of real-life and simulated data. All these methods can handle both regression and classification tasks seamlessly but all fail when it comes to dealing with data containing missing values. The implementation has shown that PERF has the best performance in the case of highly correlated data closely followed by RF. PERF and XGBOOST are "data-hungry" methods, they had the worst performance on small data sizes but they are the fastest when it comes to the execution time. SVM is the most appropriate when many redundant features are in the dataset. A surplus with the PERF is its natural cut-off at zero helping to separate positive and negative scores with all positive scores indicating essential and significant features while the negatives score indicates useless features. RF and LASSO are very versatile in a way that they can be used in almost all situations despite they are not giving the best results.

The lectures starts with defining the decision trees, the splitting criteria,and different uses of the tree like applying the algorithm to categorial features, splitting on continuous features,or using the trees for regression problems, then it explains combining multiple trees and using Ensemble Learning to apply Random Forest, in the last lecture we take a glimpse of XGBoost and how to use them, without any more details. This is probably the most hyped part of the whole specialization, I found many people celebrating that this introductory course will discuss such topics.

Another of the new machine learning algorithms in Oracle 21c Database is called XGBoost. Most people will have come across this algorithm due to its recent popularity with winners of Kaggle competitions and other similar events. XGBoost is an open source software library providing a gradient boosting framework in most of the commonly used data science, machine learning and software development languages. It has it's origins back in 2014, but the first official academic publication on the algorithm was published in 2016 by Tianqi Chen and Carlos Guestrin, from the University of Washington. The algorithm builds upon the previous work on Decision Trees, Bagging, Random Forest, Boosting and Gradient Boosting.

Machine learning software is a type of artificial intelligence (AI) that uses data to predict the outcomes of specific situations. Today machine learning technology is implemented across various business sectors to coordinate processes based on predicted outcomes. If you are looking to improve your organization's efficiency, consider leveraging machine learning tools. These platforms can help your team build machine learning models that will generate meaningful insights. In turn, this can lead to smarter business decisions and better outcomes across the organization.