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/

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.

It depends on how the labels are encoded. If onehot use CCE. If its a single integer representing the class use SCCE (Source: same as in the official (wrong) answer) From the question it's not clear how the labels are encoded. But for just 3 classes there is no doubt it's better to go with one-hot encoding. Memory restrictions or a huge number of classes might point to SCCE

You now HAVE TO to train a model with the following label map: [`˜drivers_license', `˜passport', `˜credit_card'].

If you are wondering between C & D - think about what "sparse" means It is used when dealing with hundreds of categories

mutually exclusive classes

In this case, we have a multi-class classification problem with three classes: driver's license, passport, and credit card. Therefore, we should use the categorical cross-entropy loss function to train our model. Sparse categorical cross-entropy is used for multi-class classification problems where the labels are represented in a sparse matrix format. This is not the case in this problem.

Only 3 categories of values being either T or F. They don't really need to be integer encoded, which differs sparse cross-entropy from categorical.

https://fmorenovr.medium.com/sparse-categorical-cross-entropy-vs-categorical-cross-entropy-ea01d0392d28

The correct answer is: C. Categorical cross-entropy. you are dealing with a multi-class classification problem where each image can belong to one of three classes: "driver's license," "passport," or "credit card." Categorical cross-entropy is the appropriate loss function for multi-class classification tasks. It measures the dissimilarity between the predicted class probabilities and the true class labels. It's designed to penalize larger errors in predicted probabilities and help the model converge towards more accurate predictions.