Topic 1 Question 18
3 つ選択Business owners at your company have given you a database of bank transactions. Each row contains the user ID, transaction type, transaction location, and transaction amount. They ask you to investigate what type of machine learning can be applied to the data. Which three machine learning applications can you use?
Supervised learning to determine which transactions are most likely to be fraudulent.
Unsupervised learning to determine which transactions are most likely to be fraudulent.
Clustering to divide the transactions into N categories based on feature similarity.
Supervised learning to predict the location of a transaction.
Reinforcement learning to predict the location of a transaction.
Unsupervised learning to predict the location of a transaction.
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コメント(17)
BCD makes more sense to me. Its for sure not unsupervised, since locations are in the data already. Reinforcement also doesn't fit, as there no AI and no interactions with data from the observer.
👍 65jvg6372020/03/15Answer: B, C, D Description: Fraud is not a feature, so unsupervised, location is given so supervised, Clustering can be done looking at the done with same features
👍 37[Removed]2020/03/26Anwer: BCD Things to understand: Supervised learning will only predict the column that is labeled. In this case, there is not Fraud or not Fraud column inside which he will train on. So Option A, wrong. option D: Supervised learning for column (transaction location) is possible as column exist to train on. Option C: Custering N-type is possible and also an unsupervised learning to make cluster of similar pattern. Option B: Its a weaker point here, User should be able to know which clusters are fraud in history. As it doesn't give enough information about past analysis whether user knows potential frauds or not. Ignore this option, if question asked for 2 right options only.
👍 3musumusu2023/02/23
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