Ensemble Methods
A machine learning approach that combines multiple models to produce predictions that are more accurate and robust than any individual model, using techniques like voting, averaging, or stacking.
Ensemble methods include bagging (random forests), boosting (XGBoost, LightGBM, AdaBoost), and stacking. XGBoost and LightGBM dominate Kaggle competitions and enterprise tabular data applications. In deep learning, model ensembles of 3-5 neural networks typically improve accuracy by 1-3% over single models. Major AI competitions are almost always won by ensembles. The trade-off is increased compute and complexity — running 5 models costs 5x the inference resources of a single model.
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Related Terms
Artificial General Intelligence (AGI)
A hypothetical form of AI that can understand, learn, and apply knowledge across any intellectual task at or above human level, rather than being specialized for specific tasks.
AI Alignment
The research field focused on ensuring AI systems behave in accordance with human values and intentions, particularly as systems become more capable.
Fine-Tuning
The process of further training a pre-trained AI model on a specific, smaller dataset to specialize it for a particular task or domain, requiring far less compute than training from scratch.
Foundation Model
A large AI model trained on broad data that can be adapted to a wide range of downstream tasks — examples include GPT-4, Claude, Gemini, and Llama.
Machine Learning
A subset of AI where systems learn patterns from data rather than being explicitly programmed, improving their performance on tasks through experience without human-written rules.
Model Training
The computationally intensive process of teaching an AI model by feeding it data and adjusting its parameters to minimize errors, often requiring thousands of GPUs running for weeks or months.
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