AI Engineering Degree 2025 – 400 Free Practice Questions to Pass the Exam

In multiple linear regression, especially with very large datasets, the use of an optimization approach is essential for efficiently finding the coefficients that minimize the error of the model. Optimization methods, such as gradient descent or more advanced techniques like stochastic gradient descent and coordinate descent, focus on iteratively adjusting the coefficients in a way that reduces the cost function (usually the sum of squared residuals).

These optimization techniques are particularly suited for large datasets because they can handle large matrices and high-dimensional spaces effectively, enabling convergence to a solution without requiring the entire dataset to be loaded into memory all at once. This is in contrast to direct methods such as matrix inversion, which can be computationally expensive and are often impractical for large datasets due to memory constraints.

Furthermore, optimization approaches can incorporate regularization techniques, which help to prevent overfitting—a crucial consideration in machine learning with large datasets where model complexity can quickly increase.

Other options have limitations: brute force methods are inefficient and infeasible for large datasets, computationally intensive algorithms could be too slow or require too much memory, and simple averages methods are not appropriate for capturing the relationships between multiple variables in a regression framework.