Hands-On Convolutional Neural Networks with TensorFlow

Data Imbalance

We have already seen the importance of data representation and distribution in tackling the problem of Bias and Variance. Another related problem we encounter is the unequal distribution of data among various classes in classification tasks. This is called data imbalance. For example if we have a binary classification problem and one of the classes has 50000 images and the other class has only 1000 images, this can lead to huge problems in the performance of the trained algorithm. We have to tackle this problem of imbalanced data by: