A decision tree is primarily used for classification or regression tasks, where it predicts outcomes based on input features.

The primary purpose of a hash table is to provide fast data retrieval using a hash function.

Neural networks are primarily used for pattern recognition in various case studies, allowing them to identify complex relationships in data.

The parsing table in LR parsing is used to determine the next action based on the current state and input symbol.

A queue is primarily used for level-order traversal of a binary tree, allowing nodes to be processed in the order they are encountered.

The primary purpose of a router is to route packets between different networks, making decisions based on IP addresses.

A stack is used to implement recursion iteratively, allowing for depth-first traversal of the binary tree.

SVM is primarily used for classification tasks, where it separates data into different categories using hyperplanes.

A switch connects devices within the same network, using MAC addresses to forward data to the correct destination.

An AVL tree is a self-balancing binary search tree where the difference in heights between the left and right subtrees cannot be more than one, ensuring efficient searching.

The primary purpose of balancing in AVL and Red-Black trees is to ensure O(log n) time complexity for operations.

BFS explores all vertices at the present depth level before moving on to the next level, making it suitable for finding the shortest path in unweighted graphs.

Dijkstra's algorithm is designed to find the shortest path from a source node to all other nodes in a weighted graph.

The primary purpose of dynamic programming is to optimize recursive algorithms by storing results of subproblems.

Error control is primarily used to ensure data integrity by detecting and correcting errors in transmitted data.

Evaluation metrics are used to assess how well a machine learning model performs on a given task.

Feature engineering transforms raw data into features that better represent the underlying problem, improving model performance.

HTTP methods like GET and POST are used for data retrieval and submission between clients and servers.

Intermediate code generation creates a representation of the source code that is independent of the target machine, allowing for easier optimization and code generation.

Lexical analysis converts the source code into tokens, which are the basic building blocks for further processing in the compiler.

Linear regression is used to predict a continuous outcome variable based on one or more predictor variables.

Linear regression is used to predict a continuous outcome based on one or more input features.

Linear regression is used to predict a continuous outcome variable based on one or more predictor variables.

Model deployment makes the trained model available for use in real-world applications, allowing it to make predictions on new data.

Routing is primarily used to determine the best path for data packets to travel across networks, ensuring efficient data delivery.

Subnetting is primarily used to improve network performance and security by dividing a larger network into smaller, manageable segments.

Support Vector Machines are primarily used for classification tasks, where they aim to find the optimal hyperplane that separates different classes in the feature space.

Syntax-directed translation is primarily used to produce an intermediate representation from the parse tree.

The primary purpose of the 'table' in dynamic programming is to store intermediate results to avoid redundant calculations.

The 'visited' array in Dijkstra's algorithm is used to keep track of which vertices have been processed to avoid reprocessing them.