Start by clarifying the definition of height (number of edges vs. nodes) and handling edge cases like an empty tree. Then present a recursive solution that computes the height as 1 + max(height(left), height(right)), and optionally discuss iterative approaches or optimizations.
Pro tip: Mention that recursion depth can be O(n) for skewed trees, so an iterative BFS solution may be safer in production to avoid stack overflow. Also, relate the problem to ML contexts like decision tree depth, showing domain awareness.
Ask whether height is defined by nodes or edges, and confirm the tree can be empty. This shows attention to detail and avoids ambiguity.
Explain that the height of a tree is 1 + the maximum height of its left and right subtrees, with a base case of 0 for an empty tree.
Implement the recursive function in a clean, readable manner, handling null nodes and returning the computed height.
State that time complexity is O(n) since each node is visited once, and space complexity is O(h) for recursion stack, where h is the height.
Mention iterative BFS/DFS solutions, potential stack overflow for skewed trees, and how this applies to ML (e.g., decision tree depth).
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