Am 23.09.2012 17:18, schrieb Wei LI:
Congrats to Vivek! And to Andreas for making this this
"challenge-winning" package possible :)
You should rather congratulate Olivier, as I think he wrote most of the
text code - and I didn't really write anything ;)
Best,
Wei
On Sun, Sep 23, 2012 at 3
Congrats!
2012/9/23 Wei LI
> Congrats to Vivek! And to Andreas for making this this "challenge-winning"
> package possible :)
>
> Best,
> Wei
>
>
> On Sun, Sep 23, 2012 at 3:41 AM, Vivek Sharma wrote:
>
>>
>> Thanks Olivier, Andreas. And, again to the text classification module
>> authors. skle
Congrats to Vivek! And to Andreas for making this this "challenge-winning"
package possible :)
Best,
Wei
On Sun, Sep 23, 2012 at 3:41 AM, Vivek Sharma wrote:
>
> Thanks Olivier, Andreas. And, again to the text classification module
> authors. sklearn rocks!
> I think I was quite lucky, but I'm
Thanks Olivier, Andreas. And, again to the text classification module
authors. sklearn rocks!
I think I was quite lucky, but I'm not complaining! :)
My feature set was almost the same as the char and word features that
Andreas used. I found that SVC gave me better performance than LR. And,
some n
2012/9/22 Andreas Mueller :
> On 09/22/2012 12:17 PM, Olivier Grisel wrote:
>> and to Andreas who finished in the 6th position out of 50 final submitters.
>>
>> This contest was about text classification:
>>
>>http://www.kaggle.com/c/detecting-insults-in-social-commentary
>>
>> Any feedback on
On 09/22/2012 12:17 PM, Olivier Grisel wrote:
> and to Andreas who finished in the 6th position out of 50 final submitters.
>
> This contest was about text classification:
>
>http://www.kaggle.com/c/detecting-insults-in-social-commentary
>
> Any feedback on what scikit-learn models where used,
Congratulations to Vivek also from me :)
On 09/22/2012 12:17 PM, Olivier Grisel wrote:
> and to Andreas who finished in the 6th position out of 50 final submitters.
>
Thanks Olivier.
I'll write a short blog post, but my best model is pretty boring :-/
There was a pretty big gap between the first
and to Andreas who finished in the 6th position out of 50 final submitters.
This contest was about text classification:
http://www.kaggle.com/c/detecting-insults-in-social-commentary
Any feedback on what scikit-learn models where used, which feature
extraction / blending techniques were useful
