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Answer: There are very few occurrences of mutations relative to normal samples., You expect future mutations to have similar features to the mutated samples in the database.
**Unsupervised anomaly detection** works best when: - **A. There are very few occurrences of mutations relative to normal samples** - This creates a clear distinction where mutations are the "anomalies" that deviate from the normal pattern - **D. You expect future mutations to have similar features to the mutated samples in the database** - This ensures the detection method can identify patterns consistent with known mutations **Why other options are incorrect:** - **B**: Equal occurrences would make it difficult to distinguish anomalies from normal patterns - **C**: If future mutations have different features, the model cannot learn from existing mutation patterns - **E**: Having labels would make this a supervised learning problem, not unsupervised anomaly detection
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NO.39 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? (Choose two.)
A
There are very few occurrences of mutations relative to normal samples.
B
There are roughly equal occurrences of both normal and mutated samples in the database.
C
You expect future mutations to have different features from the mutated samples in the database.
D
You expect future mutations to have similar features to the mutated samples in the database.
E
You already have labels for which samples are mutated and which are normal in the database.