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Decision Tree Prediction

MediumDecision Trees

Traverse a decision tree to make predictions.

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

  • "feature": index of the feature to check
  • "threshold": split value
  • "left": subtree for feature_value <= threshold
  • "right": subtree for feature_value > threshold

Each leaf node has:

  • "class": the predicted class label

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

Example:

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 (33) against the threshold (55).
  • Since 3≤53 \leq 5, we move to the left subtree and predict class 0. The same process applies to the third point [5], as 5≤55 \leq 5 also leads to the left subtree.
  • For the second point [7], the feature value (77) is greater than the threshold (55), 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].

Constraints:

  • tree: nested dict with feature/threshold/left/right or class keys
  • X: 2D list of feature values
  • Return list of predicted class labels
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