Jake,

it looks like you mixed up precision and recall::

  >>> print TP / float(TP + FN)  # recall
  0.948113562768
  >>> print TP / float(TP + FP)  # precision
  0.142337086782


2012/2/28 Peter Prettenhofer <[email protected]>:
> Hi Jake,
>
> the tutorial looks great! Unfortunately, I haven't got the time to go
> through all of it yet, however, I spotted something that confused me:
> in the last paragraph of Section 2.3.4 you state: "Of the points we
> label quasars, only 14% of them are correctly labeled." By "we labeled
> quasars" you mean those points who's true label is positive (=quasar)
> and not who's predicted label is positive? Because the latter would
> refer to precision rather than recall. Maybe you should change the
> wording to avoid confusion.
>
> BTW: "we are correctly identifying 95% of all quasars." should
> actually be "among the points identified as quasars 95% are correct"
> because the former would be recall rather than precision.
>
> best,
>  Peter
>
> 2012/2/27 Jacob VanderPlas <[email protected]>:
>> Thanks for all the feedback.
>> I pushed an update this morning which addressing some of the easy fixes
>> that were brought up, as well as adding the final two exercises.  Thanks!
>> http://jakevdp.github.com/tutorial/astronomy/exercises.html
>>   Jake
>>
>> Lars Buitinck wrote:
>>> 2012/2/27 Jacob VanderPlas <[email protected]>:
>>>
>>>> If you have a few minutes to look it over, I'd really appreciate some
>>>> feedback as I add the finishing touches this week.  Also, as there's no
>>>> way I'll get through all of it in a two hour tutorial, I'd like feedback
>>>> on which parts you think I should focus on!
>>>>
>>>
>>> Looks nice!
>>>
>>> Any reason not to use the sklearn.metrics module for evaluation?
>>>
>>> In "2.3.5.2. A Simple Method: Decision Tree Regression" you announce
>>> an NN method, but then actually use a decision tree.
>>>
>>>
>>
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>
>
> --
> Peter Prettenhofer



-- 
Peter Prettenhofer

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