How can you reduce the training time of a linear regression model?

Increasing the learning rate is a strategy that can potentially reduce the training time of a linear regression model. The learning rate controls how much the model weights are updated during training with each iteration. By increasing the learning rate, the updates to the model weights become larger, allowing the model to converge to the optimal solution more quickly. However, this carries some risk, as too high a learning rate can lead to overshooting the minimum during optimization, resulting in divergent behavior rather than stabilization.

Reducing the training time through this method hinges on striking a balance; if the learning rate is increased without careful tuning, it can lead to instability in the learning process. Nevertheless, when appropriately set, a higher learning rate can effectively speed up convergence, making it a viable method for reducing the overall training time of the model.

The other options, in contrast, may not effectively contribute to reducing training time. For example, switching from stochastic average gradient to batch gradient descent can result in longer training times because batch gradient descent computes the gradient using the entire dataset, whereas stochastic gradient descent updates the weights using individual data points, which can lead to faster convergence under the right conditions. Increasing the training dataset size generally results in longer training times since more data requires more computation, and switching