Model Ensembling
Model ensembling is a machine learning technique that combines multiple predictive models to produce a single, more accurate and robust prediction. It works by aggregating the outputs of individual models, often using methods like averaging, voting, or stacking, to reduce variance, bias, or overfitting. This approach is widely used in competitions and production systems to improve performance beyond what any single model can achieve.
Developers should learn model ensembling when building high-stakes machine learning applications where accuracy and reliability are critical, such as in finance, healthcare, or autonomous systems. It is particularly useful in scenarios with noisy data, complex patterns, or when individual models have complementary strengths, as it can boost predictive power and generalization. For example, it's commonly applied in Kaggle competitions to win top rankings by leveraging diverse algorithms like decision trees, neural networks, and linear models.