Peter,
Thanks for the comments
I had mixed up precision/recall in the first version, but corrected that
yesterday morning. The calculations now match the output of
metrics.precision_score() and metrics.recall_score(), so I think they're
correct.
In my phrasing "of the points we labeled quasars..." it may be more
clear to say "of the points we predicted to be quasars, only 14% are
actually quasars". I see that the text currently refers to this number
first as precision, and then as recall: I'll fix that typo.
Thanks for the close read!
Jake
On 02/28/12 01:19, Peter Prettenhofer wrote:
> 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
>
>
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