Topic 1 Question 14
2 つ選択You want to use a database of information about tissue samples to classify future tissue samples as either normal or mutated. You are evaluating an unsupervised anomaly detection method for classifying the tissue samples. Which two characteristic support this method?
There are very few occurrences of mutations relative to normal samples.
There are roughly equal occurrences of both normal and mutated samples in the database.
You expect future mutations to have different features from the mutated samples in the database.
You expect future mutations to have similar features to the mutated samples in the database.
You already have labels for which samples are mutated and which are normal in the database.
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I think that AD makes more sense. D is the explanation you gave. In the rest, A makes more sense, in any anomaly detection algorithm it is assumed a priori that you have much more "normal" samples than mutated ones, so that you can model normal patterns and detect patterns that are "off" that normal pattern. For that you will always need the no. of normal samples to be much bigger than the no. of mutated samples.
👍 66jvg6372020/03/15A instead of B: "anomaly detection (also outlier detection[1]) is the identification of rare items, events or observations which raise suspicions by differing significantly from the majority of the data
👍 21jvg6372020/03/11- 正解だと思う選択肢: AC
Anomaly detection unsupervised learning The objective of Unsupervised Anomaly Detection is to detect previously unseen rare objects or events without any prior knowledge about these. The only information available is that the percentage of anomalies in the dataset is small, usually less than 1%.
So it is A & C
👍 5certmonkey2023/02/09
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