Handling imbalanced data

In most real-world problems, our data is imbalanced, which means that the distribution of records from different classes (such as patients with and without cancer) is different. Handling imbalanced datasets is an important task in machine learning as it is common to have datasets with uneven class distribution. In such cases, the minority class is often under-represented, which can cause poor model performance and biased predictions. The reason behind this is that machine learning methods are trying to optimize their fitness function to minimize the error in the training set. Now, let’s say that we have 99% of the data from the positive class and 1% from the negative class. In this case, if the model predicts all records as positive, the error will be 1%; however, this model is not useful for us. That’s why, if we have an imbalanced dataset, we need to use various methods to handle imbalanced data. In general, we can have three categories of methods...