Decision Tree
A machine learning model that makes predictions by learning a series of if-then rules from data, creating a tree-like structure of decisions that is easy for humans to interpret.
Decision trees are among the most interpretable machine learning models, making them popular in regulated industries like healthcare and finance where explainability is required. While individual trees are prone to overfitting, ensemble methods like random forests (hundreds of trees) and gradient boosting (XGBoost, LightGBM) combine many trees for state-of-the-art tabular data performance. XGBoost has won more Kaggle competitions than any other algorithm family. Decision trees remain the go-to choice for structured business data.
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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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