What are iterative algorithms in data science?

What are iterative algorithms in data science?

Iterative algorithms are methods that repeatedly refine solutions to problems, often used in optimization and machine learning.

Can you give me an example of an iterative algorithm?

Sure! The gradient descent algorithm is a popular iterative method used to minimize a function by updating parameters in the direction of the steepest descent.

How do iterative algorithms differ from other algorithms?

Unlike non-iterative algorithms, which provide a solution in a single step, iterative algorithms improve solutions through repeated calculations.

What are the advantages of using iterative algorithms?

They can handle large datasets, converge to optimal solutions, and are often more flexible in adapting to different problems.

Are there any drawbacks to using them?

Yes, they can be computationally expensive and may converge slowly or get stuck in local minima.