Ensemble Learning
Ensemble learning is a machine learning technique that combines multiple models, called base learners or weak learners, to produce a more accurate and robust predictive model than any individual model alone. It works by aggregating the predictions from various models, such as decision trees or neural networks, to reduce variance, bias, or improve generalization. Common methods include bagging, boosting, and stacking, which are widely used in classification and regression tasks.
Developers should learn ensemble learning when building high-performance machine learning systems, especially in competitions like Kaggle or real-world applications where accuracy and stability are critical, such as fraud detection, medical diagnosis, or financial forecasting. It helps mitigate overfitting, handle noisy data, and improve model reliability by leveraging the strengths of diverse algorithms, making it essential for advanced data science and AI projects.