Coursera: Machine Learning (Week 9) Quiz - Anomaly Detection | Andrew NG

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1.Anomaly Detection.

Don't just copy & paste for the sake of completion. The solutions uploaded here are only for reference.They are meant to unblock you if you get stuck somewhere.Make sure you understand first.

1. For which of the following problems would anomaly detection be a suitable algorithm?
   
   
   •  From a large set of primary care patient records, identify individuals who might have unusual health conditions.
   •  Given data from credit card transactions, classify each transaction according to type of purchase (for example: food, transportation, clothing).
   
   
   •  Given an image of a face, determine whether or not it is the face of a particular famous individual.
   
   
   •  Given a dataset of credit card transactions, identify unusual transactions to flag them as possibly fraudulent.
   
   
   •  In a computer chip fabrication plant, identify microchips that might be defective.
   
   
   •  From a large set of hospital patient records, predict which patients have a particular disease (say, the flu).

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2. Suppose you have trained an anomaly detection system for fraud detection, and your system that flags anomalies when p(x) is less than ε, and you find on the cross-validation set that it is missing many fradulent transactions (i.e., failing to flag them as anomalies). What should you do?
   
   
   •  Increase ε
   
   
   •  Decrease ε
   
   

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2. Suppose you have trained an anomaly detection system for fraud detection, and your system that flags anomalies when p(x) is less than ε, and you find on the cross-validation set that it is mis-flagging far too many good transactions as fradulent. What should you do?
   
   
   •  Increase ε
   
   
   •  Decrease ε

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3. Suppose you are developing an anomaly detection system to catch manufacturing defects in airplane engines. You model uses
   
   You have two features  = vibration intensity, and  = heat generated. Both  and  take on values between 0 and 1 (and are strictly greater than 0), and for most “normal” engines you expect that . One of the suspected anomalies is that a flawed engine may vibrate very intensely even without generating much heat (large , small ), even though the particular values of  and  may not fall outside their typical ranges of values. What additional feature  should you create to capture these types of anomalies:
   
   
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   •       ANSWER
   
   
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4. Which of the following are true? Check all that apply.
   
   
   •  If you do not have any labeled data (or if all your data has label y = 0), then is is still possible to learn p(x), but it may be harder to evaluate the system or choose a good value of ϵ.

      If you are developing an anomaly detection system, there is no way to make use of labeled data to improve your system.

   
   
   •  When choosing features for an anomaly detection system, it is a good idea to look for features that take on unusually large or small values for (mainly the) anomalous examples.
   •  If you have a large labeled training set with many positive examples and many negative examples, the anomaly detection algorithm will likely perform just as well as a supervised learning algorithm such as an SVM.
   
   
   •  In a typical anomaly detection setting, we have a large number of anomalous examples, and a relatively small number of normal/non-anomalous examples.
   
   
   •  When developing an anomaly detection system, it is often useful to select an appropriate numerical performance metric to evaluate the effectiveness of the learning algorithm.
   •  In anomaly detection, we fit a model p(x) to a set of negative ( y=0) examples, without using any positive examples we may have collected of previously observed anomalies.
   •  When evaluating an anomaly detection algorithm on the cross validation set (containing some positive and some negative examples), classification accuracy is usually a good evaluation metric to use.
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   • 5.You have a 1-D dataset  and you want to detect outliers in the dataset. You first plot the dataset and it looks like this:
     
     Suppose you fit the gaussian distribution parameters  and  to this dataset.
     Which of the following values for  and  might you get?
     
     
     •   -3,  4   ANSWER
     
     
     •   -6,  4
     
     
     •   -3,  2
     
     
     •   -6,  2
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