> scikit-learn mailing list
> scikit-learn@python.org
> https://mail.python.org/mailman/listinfo/scikit-learn
>
--
==========
Dr. Thomas Evangelidis
Research Scientist
IOCB - Institute of Organic Chemistry and Bioch
nce for any advice.
Thomas
--
==========
Dr. Thomas Evangelidis
Research Scientist
IOCB - Institute of Organic Chemistry and Biochemistry of the Czech Academy
of Sciences <https://www.uochb.cz/web/structure/31.html?lang=en>, Prague,
Czech Republic
&
CEITEC - Cent
results but I am not satisfied. Is this the right way
to use sample_weights in RF. I would appreciate any advice or suggested
work flow.
--
==
Dr Thomas Evangelidis
Post-doctoral Researcher
CEITEC - Central European Institute
all the same and doesn't matter, and you would get
> the same splits, since R^2 is just a rescaled MSE.
>
> Best,
> Sebastian
>
> > On Mar 1, 2018, at 9:39 AM, Thomas Evangelidis <teva...@gmail.com>
> wrote:
> >
> > Hi Sebastian,
> >
> > Going b
g#Variance_reduction
Best,
Sebastian
> On Mar 1, 2018, at 8:27 AM, Thomas Evangelidis <teva...@gmail.com> wrote:
>
>
> Hi again,
>
> I am currently revisiting this problem after familiarizing myself with
Cython and Scikit-Learn's code and I have a very important query
cially supporting it. So this is an hidden feature. We could always
>> discuss to make this feature more visible and document it.
>>
>>
>>
>
--
==
Dr Thomas Evangelidis
Post-doctoral Researcher
CE
t documented since we were not officially
supporting it. So this is an hidden feature. We could always discuss to
make this feature more visible and document it.
Guillaume Lemaitre
INRIA Saclay Ile-de-France / Equipe PARIETAL
guillaume.lemai...@inria.fr - https://glemaitre.github.io/
*From: *Thomas Evange
rietal team
> Center for Data Science Paris-Saclay
> https://glemaitre.github.io/
>
> ___
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>
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==
is: is it possible to write a class that takes an arbitrary function
"loss(predictions, targets)" to calculate the loss and impurity of the
nodes?
thanks,
Thomas
--
==
Dr Thomas Evangelidis
Post-doctoral Researcher
CEITEC
J.B. via scikit-learn <
> scikit-learn@python.org> wrote:
>
>> I am also very interested in knowing if there is a sklearn cookbook
>> solution for getting the weights of a one-hidde-layer MLPClassifier.
>> J.B.
>>
>> 2017-12-07 8:49 GMT+09:00 Thomas
the
features (nuclei in this case). If there is not such an algorithm in
scikit-learn,
could you please point me to any other Python library which does that?
Thanks in advance.
Thomas
--
==
Dr Thomas Evangelidis
Post-doctoral
--
==
Dr Thomas Evangelidis
Post-doctoral Researcher
CEITEC - Central European Institute of Technology
Masaryk University
Kamenice 5/A35/2S049,
62500 Brno, Czech Republic
email: tev...@pharm.uoa.gr
teva...@gmail.com
set of 10 samples of one class, 9
> samples of the other, and the test set is composed of the class that is
> minority on the train set.
>
> G
>
> On Tue, Sep 26, 2017 at 06:10:39PM +0200, Thomas Evangelidis wrote:
> > Greetings,
>
> > I don't know if anyone en
in advance
Thomas
--
==
Dr Thomas Evangelidis
Post-doctoral Researcher
CEITEC - Central European Institute of Technology
Masaryk University
Kamenice 5/A35/2S049,
62500 Brno, Czech Republic
email: tev...@pharm.uoa.gr
loss function.
Dne 13. 9. 2017 20:48 napsal uživatel "Andreas Mueller" <t3k...@gmail.com>:
>
>
> On 09/13/2017 01:18 PM, Thomas Evangelidis wrote:
>
>
> Thanks again for the clarifications Sebastian!
>
> Keras has a Scikit-learn API with the KeraRegre
he number of
> observations and M the number of features?
>
> Both x and x' should be denoting training examples. The kernel matrix is
> symmetric (N x N).
>
>
>
> Best,
> Sebastian
>
> > On Sep 13, 2017, at 5:25 AM, Thomas Evangelidis <teva...@gmail.com>
&g
o it would be
harder to find one that minimizes my own loss function.
For the record, my loss function is the centered root mean square error.
Thanks in advance for any advice.
--
==
Dr Thomas Evangelidis
Post-doctoral Researc
We had that discussion in
the past here, yet in practice I get good correlations with the
experimental values using MLPRegressors.
> Best,
> Sebastian
>
>
> > On Sep 10, 2017, at 3:13 PM, Thomas Evangelidis <teva...@gmail.com>
> wrote:
> >
> > Greetings,
> &
--
==
Dr Thomas Evangelidis
Post-doctoral Researcher
CEITEC - Central European Institute of Technology
Masaryk University
Kamenice 5/A35/2S049,
62500 Brno, Czech Republic
email: tev...@pharm.uoa.gr
teva...@gmail.com
website: https://sites.google.com/site
ar values).
>
> Pozdrawiam, | Best regards,
> Maciek Wójcikowski
> mac...@wojcikowski.pl
>
> 2017-09-06 20:48 GMT+02:00 Thomas Evangelidis <teva...@gmail.com>:
>
>>
>> After some though about this problem today, I think it is an objective
>>
the RMSD
between the binding affinities of the overlapping molecules. Any idea how I
can do that with scikit-learn?
On 6 September 2017 at 00:29, Thomas Evangelidis <teva...@gmail.com> wrote:
> Thanks Jason, Sebastian and Maciek!
>
> I believe from all the suggestions, the most fea
you can potentially use them to improve
>> your estimates. You could also consider using experiment ID as a
>> categorical predictor in a sufficiently general regression method.
>> >
>> > Lastly, you may already know this, but the term "meta-analysis"
--
==
Dr Thomas Evangelidis
Post-doctoral Researcher
CEITEC - Central European Institute of Technology
Masaryk University
Kamenice 5/A35/2S049,
62500 Brno, Czech Republic
email: tev...@pharm.uoa.gr
teva...@gmail.com
website: https://sites.google.com/site/thomasevangelidishomepage
mas
--
==
Thomas Evangelidis
Research Specialist
CEITEC - Central European Institute of Technology
Masaryk University
Kamenice 5/A35/1S081,
62500 Brno, Czech Republic
email: tev...@pharm.uoa.gr
teva...@gmail.com
webs
>
> for both, you may find lowering values will improve performance on unseen
> data.
>
>
>
>
>
>
>
>
>
> On Tue, Jan 10, 2017 at 4:46 AM, Thomas Evangelidis <teva...@gmail.com>
> wrote:
>
>> Jacob,
>>
>> The features are not 6000
ou both of you for your hints!
best
Thomas
--
==
Thomas Evangelidis
Research Specialist
CEITEC - Central European Institute of Technology
Masaryk University
Kamenice 5/A35/1S081,
62500 Brno, Czech Republic
email: tev...@pharm
hout overfitting. In any case, if you to do the stacking, I'd probably
> insert a k-fold cv between the mlps and the meta estimator. However I'd
> definitely also recommend simpler models als
> alternative.
>
> Best,
> Sebastian
>
> On Jan 7, 2017, at 4:36 PM, Thomas Evangel
stems from
other factors that are irrelevant to this thread.
--
======
Thomas Evangelidis
Research Specialist
CEITEC - Central European Institute of Technology
Masaryk University
Kamenice 5/A35/1S081,
62500 Brno, Cz
with “refit=False” to avoid refitting if
> it helps. http://rasbt.github.io/mlxtend/user_guide/classifier/
> EnsembleVoteClassifier/#example-5-using-pre-fitted-classifiers
>
> Best,
> Sebastian
>
>
>
> > On Jan 7, 2017, at 11:15 AM, Thomas Evangelidis <teva...@gmail.com&
consensus
predictions? Can the BaggingRegressor do this job using MLPRegressors as
input?
Thanks in advance for any hint.
Thomas
--
==
Thomas Evangelidis
Research Specialist
CEITEC - Central European Institute of Technology
lecular-fingerprints.pdf
> http://pubs.acs.org/doi/abs/10.1021/ci400187y
>
> Best,
> Sebastian
>
>
> > On Dec 19, 2016, at 4:56 PM, Thomas Evangelidis <teva...@gmail.com>
> wrote:
> >
> > this means that both are feasible?
> >
> > On 19 December 201
this means that both are feasible?
On 19 December 2016 at 18:17, Sebastian Raschka <se.rasc...@gmail.com>
wrote:
> Thanks, Thomas, that makes sense! Will submit a PR then to update the
> docstring.
>
> Best,
> Sebastian
>
>
> > On Dec 19, 2016, at 11:06 AM, Thom
alues. So
my second question is: when combining both FP3 and FP4 into a single
array is there any way to designate to the MLP that the features that
correspond to FP3 must reproduce the logarithmic transform of the
experimental values while the features of FP4 the original untransformed
experimental va
It finally works with nu=0.01 or less and the predictions are good. Is
there a problem with that?
On 8 December 2016 at 12:57, Thomas Evangelidis <teva...@gmail.com> wrote:
>
>
>>
>> @Thomas
>> I still think the optimization problem is not feasible due to your dat
1 leads to:
>
> [0 1 1 1]
> 0.34660921283
>
> For comparison, I used a more vanilla MLP (1 hidden layer with 2 units and
> logistic activation as well; https://github.com/
> rasbt/python-machine-learning-book/blob/master/code/ch12/ch12.ipynb),
> essentially resulting
>
>
> @Thomas
> I still think the optimization problem is not feasible due to your data.
> Have you tried balancing the dataset as I mentioned in your other question
> regarding the
>
> MLPClassifier?
>
>
>
Hi Piotr,
I had tried all the balancing algorithms in the link that you stated, but
What was your training error there?
>
> Will the NuSVC converge when you skip the sample_weights?
>
>
> Greets,
> Piotr
>
>
> On 08.12.2016 00:07, Thomas Evangelidis wrote:
>
> Greetings,
>
> I want to use the Nu-Support Vector Classifier with the followin
--
==
Thomas Evangelidis
Research Specialist
CEITEC - Central European Institute of Technology
Masaryk University
Kamenice 5/A35/1S081,
62500 Brno, Czech Republic
email: tev...@pharm.uoa.gr
teva...@gmail.com
website: https://sites.google.com/site
gt; ValueError: specified nu is infeasible
Does anyone know what might be wrong? Could it be the input data?
thanks in advance for any advice
Thomas
--
==========
Thomas Evangelidis
Research Specialist
CEITEC - Central European
1,1,1,0,0,0, ...] # 1 indicates "active" and 0 "inactive"
On 1 December 2016 at 14:01, Thomas Evangelidis <teva...@gmail.com> wrote:
> Greetings
>
> I have grouped data which are divided into actives and inactives. The
> features are two different ty
is whether
there is any special algorithm that creates random forests from these type
of grouped data.
thanks in advance
Thomas
--
======
Thomas Evangelidis
Research Specialist
CEITEC - Central European Institute of Technolo
--
==
Thomas Evangelidis
Research Specialist
CEITEC - Central European Institute of Technology
Masaryk University
Kamenice 5/A35/1S081,
62500 Brno, Czech Republic
email: tev...@pharm.uoa.gr
teva...@gmail.com
website: https://sites.google.com/site
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