Is the Mean Squared Error (MSE) a suitable cost function for a logistic regression model?

No, Mean Squared Error (MSE) is not a suitable cost function for logistic regression. MSE is designed for regression problems where the goal is to predict a continuous value. Logistic regression, despite its name, is a classification algorithm used to predict probabilities for discrete classes. Using MSE for logistic regression can lead to a cost function that is not convex, making it difficult to find the optimal model parameters. Instead, logistic regression typically uses cost functions like Binary Cross-Entropy (Log Loss) which are specifically designed for classification tasks and produce a convex cost function.