Computational Fluid Dynamics
Machine Learning
A measure of the resources required to run an algorithm as a function of input size.
Detailed Explanation
Computational Complexity in machine learning assesses the amount of computational resources, such as time and memory, needed for an algorithm to process data. It helps evaluate an algorithm's efficiency and scalability, especially as input size increases, guiding the selection of suitable models for large-scale or resource-constrained applications. Understanding complexity ensures optimal performance and resource management.
Use Cases
- •Optimizing model selection for large datasets by evaluating their computational complexity to ensure efficient training and inference.
Related Terms
Other terms in the Machine Learning category