How to handle class imbalanced data using loss specifically made for it?

How to handle class imbalanced data using loss specifically made for it?

Above lines of code is a simple implementation of getting weights and normalising them. Here, we get the one hot values for the weights so that they can be multiplied with the Loss value separately for every class. Class balancing provides significant gains, especially when the dataset is highly imbalanced (Imbalance = 200, 100).

How is sample weighting used to handle class imbalance?

The idea is to weigh the loss computed for different samples differently based on whether they belong to the majority or the minority classes. We essentially want to assign a higher weight to the loss encountered by the samples associated with minor classes.

How does class imbalance affect machine learning algorithms?

Most of the Machine Learning algorithms are based on the inherent assumption that the data is balanced, i.e., the data is equally distributed among all of its classes. When training a model on an imbalanced dataset, the learning becomes biased towards the majority classes.

Which is the best loss function for imbalanced classification?

Cross-entropy is the go-to loss function for classification tasks, either balanced or imbalanced. It is the first choice when no preference is built from domain knowledge yet.

How does weight balancing work in deep learning?

Weight balancing balances our data by altering the weight that each training example carries when computing the loss. Normally, each example and class in our loss function will carry equal weight i.e 1.0. But sometimes we might want certain classes or certain training examples to hold more weight if they are more important.

How to balance the weighting of training examples?

Another method which we can use to balance the weighting of our training examples is the Focal Loss shown below. Here’s the main idea: in our dataset, we will naturally have some training examples that are easier to classify than others.

What happens when you use an unbalanced dataset?

In unbalanced datasets, the “Accuracy Paradox” is common. It occurs when you use the “accuracy” metric to learn the best model. Let’s continue with the example of fraud detection data.

What is the accuracy paradox of an unbalanced dataset?

In unbalanced datasets, the “ Accuracy Paradox ” is common. It occurs when you use the “accuracy” metric to learn the best model. Let’s continue with the example of fraud detection data. The algorithm will want to assign 99% of the observations to the majority class, as then the model’s accuracy will be above 90%.

Why are unbalanced datasets a challenge for machine learning?

The challenge appears when machine learning algorithms try to identify these rare cases in rather big datasets. Due to the disparity of classes in the variables, the algorithm tends to categorize into the class with more instances, the majority class, while at the same time giving the false sense of a highly accurate model.