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What happens when dataset is unbalanced?
In simple terms, an unbalanced dataset is one in which the target variable has more observations in one specific class than the others. Besides, the problem is that models trained on unbalanced datasets often have poor results when they have to generalize (predict a class or classify unseen observations).
What is unbalanced dataset?
In simple terms, an unbalanced dataset is one in which the target variable has more observations in one specific class than the others. For example, let’s suppose that we have a dataset used to detect a fraudulent transaction.
How to improve SVM performance for unbalanced dataset?
I have used SVM and applied the weighted method (in MATLAB) since the dataset is highly imbalanced. I have applied weights as inversely proportional to the frequency of data in each class. This is done on training using the command fitcsvm (trainA, trainTarg , ‘KernelFunction’, ‘RBF’, ‘KernelScale’, ‘auto’,
Why are some models more susceptible to unbalanced data?
Besides, the problem is that models trained on unbalanced datasets often have poor results when they have to generalize (predict a class or classify unseen observations). Despite the algorithm you choose, some models will be more susceptible to unbalanced data than others. Ultimately, this means you will not end up with a good model.
How to deal with an imbalanced dataset?
An imbalanced data can create problems in the classification task. Before delving into the handling of imbalanced data, we should know the issues that an imbalanced dataset can create. We will take an example of a credit card fraud detection problem to understand an imbalanced dataset and how to handle it in a better way.
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.