Project - Credit Card Fraud Detection using Machine Learning

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Understanding Class-Imbalance

Why don't we want class imbalance?

  • From our analysis, we observe there is a lot of imbalance in the classes, with most of the transactions were Non-Fraud (99.83%) of the time, while Fraud transactions occur (0.17%) of the time in the dataframe.

  • Using this imbalanced data as such is not a good idea for training a model to classify if a transaction is fraudulent or not.

  • This is because, if this imbalanced data is used to train a model, the algorithm does not have a decent amount of fraudulent-data to learn the patterns of fraudulent transactions. Thus, it most probably assumes that every transaction is non-fraudulent(the dominant class of the data).

  • This would be a pity because the model naively assumes but doesn't learn/detect the patterns in order to classify.

Any solution?

Yes! To make the dataset balanced, we could either undersample or oversample it.

  • Under-sampling: In undersampling, we reduce the dataset such that the number of samples of one class is to that of the other class. But this method has a trade-off with the amount of information lost in the form of the samples removed.
  • Over-sampling: Next is the oversampling technique. We increase the number of total samples in the dataset by generating the synthetic samples for the minority class in order to achieve the balance between both the classes. The simplest approach involves duplicating examples in the minority class, although these examples don’t add any new information to the model. Instead, new examples can be synthesized from the existing examples. This is a type of data augmentation for the minority class and is referred to as the Synthetic Minority Oversampling Technique, or SMOTE for short.

What are we going to do now?

  • We should do most pre-processing steps (splitting the data, normalization/standardization, etc) before under/over-sampling the data.

  • This is because many sampling techniques require a simple model to be trained (e.g. SMOTE uses a k-NN algorithm to generate samples). These models have better performance on pre-processed datasets (e.g. both k-NN and k-means use euclidean distance, which requires the data to be normalized).

  • So, in order for the sampling techniques to work best, we should previously perform any pre-processing steps we can. Then we shall proceed to use SMOTE technique to oversample the train data in order to use it to rain the classification algorithm.

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