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. *
Is classification accuracy a good way to measure the performance of an anomaly detection system? *
In anomaly detection, the decision boundary depends on the parameter epsilon, which we can set using the cross validation set *
In which of the following situations is it better to use anomaly detection as opposed to supervised learning? *
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? *
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. *
The multivariate Gaussian distribution models the overall probability as the product of the individual distributions p(x1)*p(x2)*...*p(xn). *
The covariance matrix models correlations between the features *
Aside from anomaly detection, where else did we encounter the covariance matrix? *
When is it better to use the original model, instead of the multivariate model? *
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