The Best New Ensemble Learning Books To Read In 2025

Feature extraction from protein sequences is a crucial task in bioinformatics. A protein sequence comprises different amino acids arranged in a specific order. Given the rapidly increasing number of protein sequences, understanding the structure and evolutionary functions of unknown protein sequences has become imperative. Therefore, there is an urgent need for the classification of protein sequences in bioinformatics.

Student dropout is a global concern, impacting individuals, educational institutions, families, and society. In the era of big data, accurate prediction of student dropout has become a pressing research topic. While previous studies have largely focused on specific education levels, such as primary, middle school, or university, there's a lack of research on ensemble learning approaches, particularly in predicting university student dropout using rare datasets.

In this paper, we introduce a novel stacking ensemble method that combines Random Forest (RF), Extreme Gradient Boosting (XGBoost), Gradient Boosting (GB), and Feed-forward Neural Networks (FNN) to predict student dropout in university courses using a dataset from Constantine the Philosopher University in Nitra. Our method outperforms base models, achieving higher testing accuracy and superior area under the curve (AUC) evaluation metrics. The significance of this study lies in its ability to identify at-risk students and influential factors. Accurate predictions motivate students to stay committed to their studies, while educational authorities can make informed decisions about student performance. This research enhances the accuracy of university student dropout prediction, reducing the risk of course abandonment and its impact on student outcomes. Even with a small dataset, selected indicators can be valuable when various performance metrics are evaluated. The predictive models use data related to students' online learning activities and partial achievements, making the methodology reliable for predicting course completion. The results from our method are instrumental in reducing the dropout rate by identifying at-risk students and influential factors. Education agents can collaborate to eradicate the issue, focusing on learner motivation strategies to improve performance and program completion. Future research avenues include exploring deep learning and other hybrid models for student dropout prediction and considering additional influential factors not covered in this study. A feature analysis study can further assist education agents in effectively addressing student dropout challenges.

Ensemble machine learning combines the power of multiple machine learning approaches, working together to deliver models that are highly performant and highly accurate. Inside Ensemble Methods for Machine Learning you will find:. Methods for classification, regression, and recommendationsSophisticated off-the-shelf ensemble implementationsRandom forests, boosting, and gradient boostingFeature engineering and ensemble diversityInterpretability and explainability for ensemble methods.

Ensemble machine learning trains a diverse group of machine learning models to work together, aggregating their output to deliver richer results than a single model. Now in Ensemble Methods for Machine Learning you’ll discover core ensemble methods that have proven records in both data science competitions and real-world applications. Hands-on case studies show you how each algorithm works in production. By the time you're done, you'll know the benefits, limitations, and practical methods of applying ensemble machine learning to real-world data, and be ready to build more explainable ML systems. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the Technology. Automatically compare, contrast, and blend the output from multiple models to squeeze the best results from your data. Ensemble machine learning applies a “wisdom of crowds” method that dodges the inaccuracies and limitations of a single model. By basing responses on multiple perspectives, this innovative approach can deliver robust predictions even without massive datasets. About the Book. Ensemble Methods for Machine Learning teaches you practical techniques for applying multiple ML approaches simultaneously. Each chapter contains a unique case study that demonstrates a fully functional ensemble method, with examples including medical diagnosis, sentiment analysis, handwriting classification, and more. There’s no complex math or theory—you’ll learn in a visuals-first manner, with ample code for easy experimentation! What’s Inside. Bagging, boosting, and gradient boostingMethods for classification, regression, and retrievalInterpretability and explainability for ensemble methodsFeature engineering and ensemble diversity. About the Reader. For Python programmers with machine learning experience. About the Author. Gautam Kunapuli has over 15 years of experience in academia and the machine learning industry. Table of Contents. PART 1 - THE BASICS OF ENSEMBLES. 1 Ensemble methods: Hype or hallelujah? PART 2 - ESSENTIAL ENSEMBLE METHODS. 2 Homogeneous parallel ensembles: Bagging and random forests. 3 Heterogeneous parallel ensembles: Combining strong learners. 4 Sequential ensembles: Adaptive boosting. 5 Sequential ensembles: Gradient boosting. 6 Sequential ensembles: Newton boosting. PART 3 - ENSEMBLES IN THE WILD: ADAPTING ENSEMBLE METHODS TO YOUR DATA. 7 Learning with continuous and count labels. 8 Learning with categorical features. 9 Explaining your ensembles.