A practical tutorial on bagging and boosting based ensembles for machine learning: Algorithms, software tools, performance study, practical perspectives and opportunities

Keyphrases

• Predictive Ability 100%
• Machine Learning Algorithms 100%
• Performance Studies 100%
• Tool Performance 100%
• Popular 50%
• Computing Paradigm 50%
• Machine Learning 50%
• Random Forest 50%
• Problem Situation 50%
• Problem Features 50%
• Ensemble Methods 50%
• Classification Task 50%
• XGBoost 50%
• Big Size 50%
• Ensemble Learning 50%
• Multi-class Problem 50%
• Boosting Algorithm 50%
• Decision Tree Ensemble 50%
• Ensemble-based 50%
• Categorical Features 50%
• Software Environment 50%
• LightGBM 50%
• Bagging Algorithm 50%
• Predictive Efficiency 50%
• Programming Language Paradigms 50%

Computer Science

• Machine Learning Algorithm 100%
• Software Development Tool 100%
• Decision Trees 33%
• computing paradigm 33%
• Machine Learning 33%
• Random Decision Forest 33%
• Classification Task 33%
• Large Data Set 33%
• Extreme Gradient Boosting 33%
• Ensemble Learning 33%
• Ensemble Technique 33%
• Multiclass Problem 33%
• Boosting Algorithms 33%
• Software Environment 33%