Search Space
In the context of data science and artificial intelligence, search space refers to the set of all possible solutions that can be generated by an algorithm to solve a given problem. It is the range of all possible combinations of parameters or variables that can be explored by an optimization algorithm to find the best solution. The search space can be either discrete or continuous, depending on the nature of the problem. For example, in a classification problem, the search space may consist of all possible combinations of features and their weights, while in a neural network, the search space may consist of all possible combinations of network architectures and hyperparameters. The size of the search space can have a significant impact on the performance of the algorithm, as a larger search space may require more computational resources and time to explore.
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