What is 'gradient descent' in machine learning?

Gradient descent is an optimization algorithm that plays a critical role in machine learning by helping to minimize a function. Specifically, it is predominantly used to minimize the cost function, which measures how well a machine learning model makes predictions compared to the actual data. By iteratively updating the model's parameters in the direction of the negative gradient of the cost function, gradient descent effectively determines the parameter values that lead to the lowest cost.

The process involves calculating the gradient (or derivative) of the function at the current parameters' values and then adjusting those parameters slightly in the opposite direction of the gradient. This iterative process continues until the algorithm reaches a point where further changes lead to little to no improvement in the cost function. This convergence to a local minimum is essential for building efficient and accurate predictive models in various machine learning tasks.

The other choices relate to different concepts within machine learning and data science. Increasing model complexity refers to creating more sophisticated models, which can lead to overfitting without proper validation. Enhancing data visualizations pertains to making data easier to interpret and does not involve optimization. Collecting user feedback is relevant for product development and user experience but is not linked to the mathematical optimization involved in machine learning model training. Therefore, the description that best captures the essence