Is the loss function for binary classification correct?
So, you can think of the binary values as probability distributions over possible classes in which case the loss function is absolutely correct and the way to go for the problem. Hope that helps.
How to solve binary classification problems in deep?
CategoricalCrossentropy: Computes the cross-entropy loss between the labels and predictions. We use this cross-entropy loss function when there are two or more label classes. We expect labels to be provided in a one-hot representation. If you want to provide labels as integers, please use SparseCategoricalCrossentropy loss.
How are non convex loss functions used in classification?
Such non-convex loss functions have been shown to be useful in dealing with outliers in classification. For all loss functions generated from (2), the posterior probability . Such loss functions where the posterior probability can be recovered using the invertible link are called proper loss functions.
When to use the hinge loss function in classification?
The hinge Loss function is meant to be used with binary classification where the target values are within the set, So use the Hinge Loss function, it must make sure that the target variable must be modified to possess values within the set rather than as just in case of Binary Cross Entropy.
When to use binary cross entropy in classification?
Binary cross-entropy a commonly used loss function for binary classification problem. it’s intended to use where there are only two categories, either 0 or 1, or class 1 or class 2. it’s a loss function that’s utilized in binary classification tasks.
Is the loss function fine in classification problem in PyTorch?
In the above case , what i’m not sure about is loss is being computed on y_pred which is a set of probabilities ,computed from the model on the training data with y_tensor (which is binary 0/1). Is this way of loss computation fine in Classification problem in pytorch? Shouldn’t loss be computed between two probabilities set ideally ?