#1




Questions on Lecture 9 (Linear Models II)
1. In the example in the lecture, we were cautioned against data snooping since looking at data can mean that we can be implicitly doing some learning in our head. My question is: Is it legitimate to look at DataSet 1 to identify my predictors, and then train on DataSet 2 with samples entirely different from DataSet 1? Of course, the out of sample error will be evaluated on DataSet 3 different from 1 and 2.
2. At the end of the lecture, somebody asked a question about multiclass classifiers and it was answered that it is commonly done using either onevsall training or onevsone training. My questions:
3. We used cross entropy error for logistic and squared error for linear. It was explained that the choice of error is so that the math becomes easy with respect to implementation of the minimization. In both cases, the practical interpretation was explained and it appears intuitive. My questions:

#2




Re: Questions on Lecture 9 (Linear Models II)
Quote:
Quote:
Quote:
__________________
Where everyone thinks alike, no one thinks very much 
#3




Re: Questions on Lecture 9 (Linear Models II)
Quote:
Statisticians don't like squared error much. It seems that minimizing the sum of absolute values of differences, instead of the square, gives better results, but the math is harder. Least squares is too sensitive to one outlier, for example. 
Tags 
data snooping, error, generalization error, multiclass classifiers 
Thread Tools  
Display Modes  

