It is strange. I use sparse matrices with LinearSVC all the time.
can you provide some code example?
---
Caleb
> On 24 Jul, 2014, at 1:46 pm, "Pagliari, Roberto"
> wrote:
>
> Is it possible to use scipy sparse matrices with LinearSVC?
>
> I tried and it does not
compare SVM to tesseract?
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regards,
Caleb
> On 24 May, 2014, at 12:36 am, klo uo wrote:
>
> Replaying to myself...
>
> The cause for reported "problem" is that classifier samples have empty strips
> on both sides,
I tried install the master branch from github
>>> pip3 install git+https://github.com/scikit-learn/scikit-learn.git
and it works! Wondering what happened?
On Friday, May 16, 2014 7:36 PM, Caleb wrote:
Hi Folk,
I have recently install python3 via homebrew, and all the libra
b5gpt7qd34z4gn/T/pip-e1h0ovom-record/install-record.txt
--single-version-externally-managed --compile failed with error code 1 in
/private/var/folders/rl/k7prlkm91gjcb5gpt7qd34z40000gn/T/pip_build_cat/scikit-learn
Can anyone help?
Regards,
Caleb
-
>Hi, please repeat your experiments several times because both >Random Forest
>Embedding > and your resampling are ?>not deterministic algorithm. their
>results may varys a lot
>please also check the attributes "n_support_" >and "support_vectors_" in the
>object, they're > highly related to >tim
iginal data. Since T1(data) and
T2(data) are both sparse matrix of similar size, I am wondering what are the
other factors that make the training time significantly shorter?
---
Caleb--
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increase in accuracy
might not be surprised. But I wonder why the drastic drop in training time. Any
idea?
--
Caleb
On Sunday, March 2, 2014 11:34 PM, Olivier Grisel
wrote:
Inverting 0 and 1 does not change the problem mathematically but does
change the number of multiplication addition
mething to do with the difficulty
of the classification problem. By simply inverting the 0 and 1, do we actually
affect the difficulty? Or maybe I understand it wrongly?
--
Caleb
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sparse data?
Regards,
Caleb
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enables me to investigate why the result is worst easily.
Just my 2 cents.
Caleb
On Wednesday, January 22, 2014 1:15 PM, Maheshakya Wijewardena
wrote:
Hi,
I have been using Scikit-learn One hot encoder for data encoding and the
resulting array supports only for a few models such as logistic
would be best if I don't have to write the code from scratch and reuse as
much code in the library as possible.
Thanks for any suggestion.
Regards,
Caleb--
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from the root node down to this leaf node?
Any idea?
-
Caleb
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dot file which contains all the information I want. But instead of parsing the
dot file, is there a more direct way to get what I want?
Thanks
-
Caleb--
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