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/

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

C needs the target to be One hot encoded already. Since it is not, the answer is D

C) Multi-Class Classification (Three or More Classes): Since you have three classes, you should use a multi-class loss function. The most common choice for multi-class image classification is categorical cross-entropy2. Categorical cross-entropy is designed for scenarios where each input belongs to exactly one class (i.e., mutually exclusive classes). Therefore, the correct answer is C. Categorical cross-entropy. It’s well-suited for multi-class classification tasks like this one. References: How to Choose Loss Functions When Training Deep Learning Neural Networks (https://machinelearningmastery.com/how-to-choose-loss-functions-when-training-deep-learning-neural-networks/) Stack Exchange: How to know which loss function is suitable for image classification? (https://datascience.stackexchange.com/questions/58138/how-to-know-which-loss-function-is-suitable-for-image-classification)

I'd go with C. Categorical cross entropy is used when classes are mutually exclusive. If the number of classes was very high, then we could use sparse categorical cross entropy.

Use sparse categorical crossentropy when your classes are mutually exclusive (e.g. when each sample belongs exactly to one class) and categorical crossentropy when one sample can have multiple classes or labels are soft probabilities (like [0.5, 0.3, 0.2]).

C D is for integer value instead of one-hot encoded vectors, in our question, it is 'drivers_license', 'passport', 'credit_card' one-hot.