CS 451 Quiz 23
Anomaly detection and multivariate Gaussians
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When evaluating an anomaly detection system, we have no positive examples in the training set, but a small number of positive examples in each of CV and test sets.
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True
False
Is classification accuracy a good way to measure the performance of an anomaly detection system?
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Yes, because we have labels in CV / test sets
No, because we do not have labels in CV / test sets
No, because of skewed classes
In anomaly detection, the decision boundary depends on the parameter epsilon, which we can set using the cross validation set
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True
False
In which of the following situations is it better to use anomaly detection as opposed to supervised learning?
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When we have similar numbers of positive and negative training examples
When there are few positive examples
When the positive examples have little in common
Spam detection
Fraud detection
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In the video, Andrew Ng proposes to replace a feature x with functions like log(x + c) or x^c in order to make them "more Gaussian". What does he suggest to compute to guide this process?
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The mean and variance of the transformed feature
The histogram of the transformed feature
The PCA of the transformed feature
If an anomaly detection system fails to assign a low value of p for an anomalous event, how could this be addressed? Check all that apply.
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Adding a new feature that captures a novel aspect of the training data
Adding a new feature that is the ratio of two existing features
Changing epsilon
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The multivariate Gaussian distribution models the overall probability as the product of the individual distributions p(x1)*p(x2)*...*p(xn).
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True
False
The covariance matrix models correlations between the features
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True
False
Aside from anomaly detection, where else did we encounter the covariance matrix?
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Neural nets
K means
PCA
When is it better to use the original model, instead of the multivariate model?
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When there are correlations between different features
When n is very large
When m is very large
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