Explain the role of the following factors in reaching global minima with a gradient descent algorithm for linear regression.a. Epochsb. Learning ratec. Parametersd. Bias and Variance

a. Epochs: An epoch is a complete pass through the entire training dataset. The number of epochs is a hyperparameter that defines the number times that the learning algorithm will work through the entire training dataset. More epochs mean that the algorithm has more opportunities to correct its errors and find the global minimum.

b. Learning Rate: The learning rate is a tuning parameter in an optimization algorithm that determines the step size at each iteration while moving toward a minimum of a loss function. A smaller learning rate could get closer to the global minimum but would require more iterations, while a larger learning rate may allow the algorithm to converge faster but risks overshooting the global minimum.

c. Parameters: In the context of linear regression, parameters are the coefficients of the predictors. The goal of gradient descent is to find the optimal values of these parameters that minimize the loss function, which is the difference between the predicted and actual output. The algorithm starts with initial values and iteratively adjusts them to move towards the global minimum.

d. Bias and Variance: Bias is the simplifying assumptions made by the model to make the target function easier to approximate. Variance is the amount that the estimate of the target function will change given different training data. The bias-variance tradeoff is the point where we are adding just noise by adding model complexity (flexibility). The training error goes down as it has to, but the test error is starting to go up. The model after the bias tradeoff begins to overfit.