Subproblems

In Data Science, subproblems refer to breaking down a complex problem into smaller, more manageable parts that can be solved independently. This approach is often used in machine learning, where a large dataset is divided into smaller subsets, and models are trained on each subset separately. Subproblems can also be used in optimization problems, where a large problem is divided into smaller subproblems that can be solved iteratively. By breaking down a problem into subproblems, it becomes easier to solve, and the solutions to the subproblems can be combined to solve the original problem.
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