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Answer: Ensemble methods like Random Forest, for their robustness to imbalanced datasets through aggregating predictions from multiple decision trees., Decision Tree, for its interpretability and ease of tuning to focus on the minority class.
Correct Option: C. Ensemble methods like Random Forest Explanation: Ensemble methods, such as Random Forest, are particularly effective for imbalanced datasets like in fraud detection scenarios. They work by aggregating the predictions of multiple decision trees, each trained on different data subsets. This approach mitigates the effects of class imbalance, enhancing the model's performance in predicting the minority class (fraudulent transactions) without significantly compromising computational efficiency and scalability. Why other options are less suitable: - A. Naive Bayes: While fast and simple, it often underperforms in imbalanced datasets by not adequately capturing the nuances of the minority class. - B. Support Vector Machine (SVM): Although powerful for classification, SVMs may not inherently address the imbalance issue and can be computationally intensive for very large datasets. - D. Decision Tree: Single decision trees are prone to bias towards the majority class in imbalanced scenarios, making them less ideal despite their interpretability. - E. Combining C and D: While both have merits, Random Forest inherently includes the benefits of multiple decision trees, making the additional focus on a single Decision Tree unnecessary for this scenario.
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In the context of developing a machine learning model for a fraud detection system, where the dataset is highly imbalanced with fraud cases representing less than 1% of all transactions, which of the following model types would be most effective in accurately identifying fraudulent transactions while also considering computational efficiency and scalability? Choose the best option.
A
Naive Bayes, due to its simplicity and speed in training, making it suitable for large datasets.
B
Support Vector Machine (SVM), with a linear kernel for its ability to handle high-dimensional data efficiently.
C
Ensemble methods like Random Forest, for their robustness to imbalanced datasets through aggregating predictions from multiple decision trees.
D
Decision Tree, for its interpretability and ease of tuning to focus on the minority class.
E
Both C (Ensemble methods like Random Forest) and D (Decision Tree), as combining their strengths can offer a balanced approach to handling imbalanced datasets.