The worst-case time complexity of insertion sort occurs when the array is sorted in reverse order, resulting in O(n^2).

The worst-case time complexity of Merge Sort is O(n log n), which is consistent regardless of the input data.

The worst-case time complexity of Quick Sort occurs when the pivot selection is poor, leading to O(n^2) performance.

The worst-case time complexity of quicksort occurs when the pivot selection is poor, leading to O(n^2) performance.

In the worst case, the time complexity for searching in a binary search tree is O(n), which occurs when the tree is skewed.

The worst-case time complexity of binary search in a sorted array is O(log n).

In the worst case, you may need to check every element in the array, leading to a time complexity of O(n).

The worst-case time complexity of sorting a stack using another stack is O(n^2) due to the need to repeatedly pop and push elements.

The worst-case time complexity of depth-first search (DFS) on a binary tree is O(n), where n is the number of nodes in the tree.

Tokenization is the process of splitting text into individual words or phrases, which are called tokens.

Transfer learning involves using a pre-trained model on a new but related task, which can save time and resources.

Transfer learning involves using a pre-trained model on a new but related task, allowing for faster training and improved performance.

HTTP operates at the Application layer of the OSI model, which is responsible for providing network services to end-user applications.

The Transport Layer (Layer 4) is responsible for end-to-end communication and error recovery, ensuring complete data transfer.

The Transport Layer (Layer 4) is responsible for end-to-end communication and ensures complete data transfer.

The Network Layer is responsible for routing packets between devices across different networks.

The Transport Layer in the TCP/IP model corresponds directly to the Transport Layer of the OSI model.

Accuracy is a common metric used to evaluate the performance of a classification model, indicating the proportion of correct predictions.

The silhouette score measures how similar an object is to its own cluster compared to other clusters, providing insight into clustering quality.

Accuracy is a common metric used to evaluate the performance of classification models like Decision Trees.

To perform binary search on a rotated sorted array, the mid-point calculation must be adjusted to account for the rotation.

For binary search to work, the data must be sorted. This allows the algorithm to effectively eliminate half of the search space with each comparison.

Rotations (single or double) are performed to maintain the balance of an AVL tree after insertion.

Decision Trees help visualize and understand the factors that contribute to credit risk in scoring.

Neural networks are used in autonomous vehicles for path planning and detecting obstacles, enabling safe navigation.

Neural networks can analyze historical financial data to predict future market trends and assist in investment decisions.

Neural networks analyze user behavior and preferences to create user profiles, which are essential for personalized recommendations.

Stacks are used in backtracking algorithms to keep track of the path taken, allowing the algorithm to revert to previous states.

Backpropagation is the algorithm used to update the weights of the network by calculating the gradient of the loss function.

The 'C' parameter in SVM controls the trade-off between maximizing the margin and minimizing classification errors, influencing the model's complexity.