ML Fundamentals
Random Forest
An AI that builds hundreds of decision trees and lets them vote on the answer, wisdom of the (tree) crowd.
Definition
An ensemble learning method that constructs many decision trees during training and outputs the mode (classification) or mean (regression) of the individual trees. Resistant to overfitting.
Why it matters
Consistently one of the best "off-the-shelf" algorithms for tabular data, often beats deep learning on structured datasets.
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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