ML Fundamentals
Ensemble Methods
Combining predictions from multiple AI models to get a better answer, like asking three doctors instead of one.
Definition
Techniques that combine multiple models to produce a prediction that is more accurate and robust than any single model. Includes bagging (Random Forests), boosting (XGBoost), and stacking.
Why it matters
Consistently top leaderboards in ML competitions; most production ML systems use ensembles for reliability.
Related terms in ML Fundamentals
Activation Functions
The "switch" inside a neural network that decides whether a neuron should fire, allowing the AI to learn complex non-linear patterns.
Active Learning
A technique where the AI asks humans to label only the most confusing examples, saving time and money on data labeling.
Anomaly Detection
Finding the "weird" stuff in a dataset, like a credit card charge in a foreign country or a broken machine part.
Artificial General Intelligence (AGI)
A hypothetical "super-AI" that can learn and do any intellectual task a human can do, not just one specific thing.
From vocabulary to outcomes
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