You work for a major banking firm as a machine learning specialist.
As part of the bank's fraud detection team, you build a machine learning model to detect fraudulent transactions.
Using your training dataset, you have produced a Receiver Operating Characteristic (ROC) curve, and it shows 99.99% accuracy.Your transaction dataset is very large, but 99.99% of the observations in your dataset represent non-fraudulent transactions.
Therefore, the fraudulent observations are a minority class.
Your dataset is very imbalanced. You have the approval from your management team to produce the most accurate model possible, even if it means spending more time perfecting the model.
What is the most effective technique to address the imbalance in your dataset?
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A. B. C. D.The correct answer is A. Synthetic Minority Oversampling Technique (SMOTE) oversampling.
Imbalanced datasets are a common challenge in machine learning, and it can be particularly challenging when the minority class represents an important or critical outcome, such as fraud detection. In this scenario, accuracy may not be the best metric to evaluate the performance of the model. Instead, you may want to consider metrics such as precision, recall, and F1-score, which take into account the number of true positives, false positives, true negatives, and false negatives.
In this case, the ROC curve indicates that the model has high accuracy, but it is important to check if this is due to a good performance on the majority class only. In imbalanced datasets, it is common for the model to perform well on the majority class but poorly on the minority class.
One common technique to address the imbalance in the dataset is oversampling. Oversampling aims to balance the classes by increasing the number of instances in the minority class. However, random oversampling may lead to overfitting and poor generalization performance, especially when the minority class is very small.
A more effective technique is SMOTE oversampling. SMOTE generates new synthetic samples by interpolating between existing minority class samples. Specifically, SMOTE selects a random minority class sample and finds its k-nearest neighbors in the feature space. It then creates new samples by interpolating between the original sample and its neighbors. The number of new samples generated can be adjusted based on the desired balance between the classes.
SMOTE oversampling can improve the performance of the model on the minority class without overfitting, and it has been shown to be effective in many real-world applications, including fraud detection.
Option C, Generative Adversarial Networks (GANs) oversampling, is not a common technique to address imbalanced datasets, and it is more computationally intensive and requires more expertise to implement than SMOTE oversampling. GANs can generate new samples by training a generator network to produce samples that are similar to the minority class, and a discriminator network to distinguish between real and generated samples. However, GANs require a lot of tuning and experimentation to obtain good results.
Option D, Edited Nearest Neighbor (ENN) undersampling, removes instances from the majority class that are near instances from the minority class. ENN can be effective when there is a lot of overlap between the classes in the feature space, but it may also remove useful information and lead to underfitting.
In summary, SMOTE oversampling is the most effective technique to address the imbalance in the dataset and improve the performance of the model on the minority class in this scenario.