Exam Professional Machine Learning Engineer topic 1 question 10 discussion

Answer is C

answer is D https://machinelearningmastery.com/how-to-choose-loss-functions-when-training-deep-learning-neural-networks/

Answer is D. The official Professional MLE Study Guide by Mona Mona has a similar question in Chapter 7 Model Building (Page 138) and their explanation in the answers section is "In case of multiclass classification problems, we use sparse categorical cross-entropy". Their reference material in the book is page 128 "Use sparse categorical cross-entropy when your classes are mutually exclusive (when each sample belongs exactly to one class) and categorical cross-entropy when one sample can have multiple classes or labels".

'driver licence' may be more common than others, which can cause the model to more easily predict the common classes while inaccurately predicting the uncommon classes.

This is also for multi-class classification, but it's used when your labels are integer encoded (e.g., 0 for driver's license, 1 for passport, 2 for credit card) rather than one-hot encoded. While you could use this if you converted your labels, given the explicit label map as a list, categorical cross-entropy assumes a one-hot encoding which is common practice.

OHE needed here as values can be 001, 010, 100 as or condition used for image.

It should be categorical cross entropy

Since the problem is a multi-class classification task (choosing between drivers_license, passport, and credit_card), you need a loss function designed for multi-class classification. Sparse Categorical Cross-Entropy is the best choice because: The labels are integer-encoded (not one-hot encoded). It is computationally more efficient than Categorical Cross-Entropy when dealing with class indices instead of one-hot vectors. Why not the others? A (Categorical Hinge): Used for multi-class classification with hinge loss, typically for SVMs, not neural networks. B (Binary Cross-Entropy): Used for binary classification (two classes), while this problem has three classes. C (Categorical Cross-Entropy): Works for multi-class classification but requires one-hot encoded labels, whereas the dataset likely uses integer labels.

Categorical cross-entropy is suitable for multi-class classification problems where each instance belongs to one and only one class. Since you have multiple classes (driver's license, passport, credit card), this loss function is appropriate.

In case of multiclass classification problems, we use sparse categorical cross‐entropy.

Because you have a multi-class classification problem with mutually exclusive classes and a label map, categorical cross-entropy is the most suitable and commonly used loss function.

The answer is C. No need to overthink it as sparse categorical cross entropy is used for sparse matrix which is not the case.

Answer will be D if the label values are integer but it is string, I will go for C

The label map [driver's_license, passport, credit_card] naturally maps to 0, 1, 2 as per machine learning standards. Which is used in Sparse categorical cross-entropy

It is C. D will be appropriate only if the labels are integers which is not true in this case.

The question clearly says "You now have to train a model with the following LABEL MAP". Label map is not one-hot encoding.

ㅇ Categorial Cross-Entropy for the multiple classification with one-hot-encoding labels ㅇ Sparse Categorical Cross-Entropy for the multiple classification with index labels