Machine Learning

Decision Trees

Splits data by asking one question at a time. The building block of forests and boosting.

At each node the tree picks the feature and threshold that best separate the outcomes, then recurses. The result reads like a flowchart, which makes it the easiest model to explain to a non-technical stakeholder.

A single deep tree memorises its training data, so trees are almost always used in groups — averaged in a random forest, or added in sequence in gradient boosting, which remains the strongest approach for tabular problems.

Also in Machine Learning

Supervised Learning Unsupervised Learning K-Means K-Nearest Neighbours Logistic Regression Linear Regression

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