AI Engineering Degree Practice Exam 2025 - Free AI Engineering Practice Questions and Study Guide

In the context of training a logistic regression model, the process involves specific steps that focus primarily on model optimization and understanding the underlying relationships within the training data.

Calculating the cost function gradient is essential because it helps determine how to adjust the model's parameters to minimize the error between predicted and actual values. Initializing the parameters is a foundational step, as it sets the starting point for the optimization algorithm. Using the cost function on the training set is necessary for evaluating how well the model performs given its current parameters, guiding the iterative optimization process.

While splitting the dataset into training and test sets is a crucial part of the machine learning workflow, it is not specifically a step in the training process of the logistic regression model itself. This step is more about preparing the data for validation rather than directly influencing the training of the specific model. Thus, it separates model evaluation from model training, making it the correct choice as not being a specific step in the training of a logistic regression model.