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Decision Tree Prediction

Traverse a decision tree to make predictions.

A decision tree is represented as nested dictionaries. Each internal node has:

Each leaf node has:

Given a tree and a list of data points (each a list of feature values), return the predicted class for each point.

Input:

tree = {"feature": 0, "threshold": 5, "left": {"class": 0}, "right": {"class": 1}}
X = [[3], [7], [5]]

Output:

[0, 1, 0]

Reasoning:

The decision tree is traversed for each data point in X. For the first point [3], we check the feature value at index 0 ( $3$ ) against the threshold ( $5$ ).
Since $3 \leq 5$ , we move to the left subtree and predict class 0. The same process applies to the third point [5], as $5 \leq 5$ also leads to the left subtree.
For the second point [7], the feature value ( $7$ ) is greater than the threshold ( $5$ ), so we move to the right subtree and predict class 1.
The predicted classes for all points are collected to form the output list: [0, 1, 0].

Editor

Python 3.13.1

0/0

Run code to see test results.

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