On 05/08/2012 10:41 AM, Gael Varoquaux wrote:
> On Mon, May 07, 2012 at 03:50:00PM -0400, Félix-Antoine Fortin wrote:
>> I presumed there are valid reasons for using a numpy array, and represent 
>> error points as -1.
> Reasons for using a numpy array are that it is faster and more memory
> efficient than a list.
>
For that reason we use numpy arrays for as much as possible (which is 
nearly everything)
in sklearn. We discussed the representation of missing values / errors 
and we could not
come up with a better (feasible) way than -1.


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