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The base case $f(1) = f(2) = 1$ determines where the recursion stops, meaning certain branches halt splitting at the bottom level. While this allows the instructor to omit drawing the entire tree in practice, the theoretical complexity analysis still estimates the scale based on the full binary tree's node count.

Conditions: The recurrence relation is $f(n) = f(n-1) + f(n-2)$.; The base cases are $f(1) = 1$ and $f(2) = 1$.; Complexity analysis uses a full binary tree for theoretical estimation.

The base case $f(1) = f(2) = 1$ determines where the recursion stops, meaning certain branches halt splitting at the bottom level. While this allows the instructor to omit drawing the entire tree in practice, the theoretical complexity analysis still estimates the scale based on the full binary tree's node count.

Conditions: The recurrence relation is $f(n) = f(n-1) + f(n-2)$.; The base cases are $f(1) = 1$ and $f(2) = 1$.; Complexity analysis uses a full binary tree for theoretical estimation.

The video calls the time complexity $O(2^n)$ "exponential complexity". This terminology is used to classify complexities that grow as a power of 2 with respect to the input size $n$.

Conditions: The complexity has the form $O(2^n)$ or similar exponential growth.; The context is the naming and classification of the derived time complexity.

The video calls the time complexity $O(2^n)$ "exponential complexity". This terminology is used to classify complexities that grow as a power of 2 with respect to the input size $n$.

Conditions: The complexity has the form $O(2^n)$ or similar exponential growth.; The context is the naming and classification of the derived time complexity.