ML Fundamentals
Bias-Variance Tradeoff
The balancing act between a model that's too simple (misses patterns) and one that's too complex (memorizes noise).
Definition
A fundamental ML concept: bias is error from oversimplified assumptions (underfitting); variance is error from sensitivity to training data fluctuations (overfitting). Optimal models balance both.
Why it matters
Understanding this tradeoff is essential for diagnosing why a model underperforms and choosing the right fix.
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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