Quick Sort has the best average-case time complexity of O(n log n) among the given sorting algorithms.

Bubble Sort has a worst-case time complexity of O(n^2).

Merge Sort is a sorting algorithm that uses the divide and conquer strategy to sort elements.

Counting Sort is not a comparison-based sort; it uses counting to determine the position of each element.

Counting Sort is a non-comparison-based sorting algorithm that sorts integers by counting occurrences.

Merge Sort requires additional space for merging, making it not an in-place sorting algorithm.

Quick Sort is not a stable sorting algorithm because it can change the relative order of equal elements.

Merge Sort is a stable sorting algorithm, meaning it maintains the relative order of equal elements.

AVL trees provide faster lookups than Red-Black trees due to their stricter balancing, but Red-Black trees are generally more efficient for insertions and deletions.

The statement that AVL trees can have nodes with two children only is false; they can have nodes with zero, one, or two children.

AVL trees can become unbalanced after every insertion, requiring rotations to restore balance, but they maintain a stricter balance than Red-Black trees.

BFS uses a queue, not a stack, which is a key difference from DFS.

DFS can be more memory efficient than BFS in certain scenarios, especially in deep graphs.

BFS explores nodes level by level, ensuring that all nodes at the present depth are explored before moving deeper.

Binary search cannot be effectively used on linked lists due to their non-contiguous memory allocation, making it inefficient compared to arrays.

Binary search requires the array to be sorted to function correctly.

A binary tree is defined as a tree data structure in which each node has at most two children, often referred to as the left and right child.

Decision Trees are sensitive to small changes in the data, which can lead to different splits and thus different models.

DFS can be implemented using recursion, which is a common approach in many algorithms.

Dijkstra's algorithm always finds the shortest path in graphs with non-negative weights.

Heap Sort is an in-place sorting algorithm, meaning it requires only a constant amount of additional space.

Intermediate code can be optimized before final code generation, allowing for better performance on the target architecture.

K-means can be sensitive to the initial placement of centroids, which can lead to different clustering results on different runs.

Quick Sort can be implemented using a linked list, but it is not stable and does not always select the median as pivot.

Random Forests can be used for both classification and regression, are less interpretable than single trees, and require more computational resources.

In Red-Black trees, a red node cannot have a red child, which means they can have a maximum of one consecutive red node.

RNNs can learn from past information in sequences, making them effective for tasks involving temporal data.

Stacks can indeed be implemented using both arrays and linked lists, while they follow LIFO (Last In, First Out) order.

SVM is sensitive to outliers, as they can affect the position of the decision boundary.

AVL trees do not allow duplicate values; they maintain a strict binary search tree property.