Hi Sanant,
On Thursday, May 26, 2016, Startup Hire wrote:
> Hi all,
>
> Hope you are doing good.
>
I would like to think so, but you never know where ML will lead us ...
>
> I am working on a project where I need to do the following things:
>
> 1. I need to fit a lognormal distribution to a s
probably, especially if they are normalised.
you have the formulas for those, right? then you can say it for sure. just
take the log on both sides. start by plotting the log of both of those
distributions and you willprobably see already
On Friday, June 3, 2016, Startup Hire wrote:
> Hi,
>
> Any
Regards,
> Sanant
>
> On Fri, Jun 3, 2016 at 3:08 PM, Michael Eickenberg <
> michael.eickenb...@gmail.com> wrote:
>
>> probably, especially if they are normalised.
>> you have the formulas for those, right? then you can say it for sure.
>> just take the log
hmm, not an answer, and off the top of my head:
if you normalize your data points to l2 norm equal 1, and then use standard
kmeans with euclidean distance (which then amounts to 2 - 2 cos(angle
between points)) would this be enough for your purposes? (with a bit of
luck there may even be some sort
text/document_clustering.html
>>
>> if your inputs are normalized, sklearn's kmeans behaves like sperical
>> kmeans (unless I'm misunderstanding something, which is certainly possible,
>> caveat lector, &c )...
>> On Jun 27, 2016 12:13 PM, "Michael
lly provide any benefit over
> sklearn.preprocessing.normalize)
>
> On 28 June 2016 at 09:20, Michael Eickenberg > wrote:
>
>> You could do
>>
>> from sklearn.pipeline import make_pipeline
>> from sklearn.preprocessing import Normalizer
>> from sk
On Tuesday, July 5, 2016, Joel Nothman wrote:
> Jaidev is suggesting that fit_intercept=False makes no sense if the data
> is sparse.
>
+1
> But I think that depends on your target variable.
>
+1
>
>
>
> On 4 July 2016 at 22:11, Alexandre Gramfort <
> alexandre.gramf...@telecom-paristech.fr
On Monday, August 1, 2016, Andreas Mueller wrote:
> Hi.
> The best is probably to use a virtual environment or conda environment
> specific for this changed version of scikit-learn.
> In that environment you could just run an "install" and it would not mess
> with your other environments.
+1!
There are several ways of achieving this. One is to build scikit-learn in
place by going into the sklearn clone and typing
make in
or alternatively
python setup.py build_ext --inplace # (i think)
Then you can use the environment variable PYTHONPATH, set to the github
clone, and python will gi
That should totally depend on your dataset. Maybe it is an "easy" dataset
and not much regularization is needed.
Maybe use PCA(n_components=2) or an LDA transform to take a look at your
data in 2D. Maybe they are easily linearly separable?
Sklearn does not do any feature selection if you don't as
Here is a possibly useful comment of larsmans on stackoverflow about
exactly this procedure
http://stackoverflow.com/questions/26604175/how-to-predict-a-continuous-dependent-variable-that-expresses-target-class-proba/26614131#comment41846816_26614131
On Mon, Oct 10, 2016 at 4:04 PM, Sean Violant
Dear Anaël,
if you wish, you could add a line to the example verifying this
correspondence. E.g. by moving the print function from between the two
silhouette evaluations to after and also evaluating that average and
printing it in parentheses.
Probably not necessary though. A comment would do als
Dear Alessio,
if it helps, the implementation quite strictly follows what is described in
GPML: http://www.gaussianprocess.org/gpml/chapters/
https://github.com/scikit-learn/scikit-learn/blob/412996f09b6756752dfd3736c306d46fca8f1aa1/sklearn/gaussian_process/gpr.py#L23
Hyperparameter optimization
You have to set a bigger \nu.
Try
nus =2 ** np.arange(-1, 10) # starting at .5 (default), going to 512
for nu in nus:
clf = svm.NuSVC(nu=nu)
try:
clf.fit ...
except ValueError as e:
print("nu {} not feasible".format(nu))
At some point it should start working.
Hope th
feasible due to your data.
> Have you tried balancing the dataset as I mentioned in your other question
> regarding the MLPClassifier?
>
>
> Greets,
> Piotr
>
>
>
>
>
>
> On 08.12.2016 10:57, Michael Eickenberg wrote:
>
> You have to set a bigger \nu
Is maybe this contrib what you are looking for? Take a close look to see
whether it does what you expect.
http://contrib.scikit-learn.org/imbalanced-learn/auto_examples/over-sampling/plot_smote.html
On Tue, Jan 10, 2017 at 6:36 PM, Suranga Kasthurirathne <
suranga...@gmail.com> wrote:
>
> Hi a
Dear Afarin,
scikit-learn is designed for predictive modelling, where evaluation is done
out of sample (using train and test sets).
You seem to be looking for a package with which you can do classical
in-sample statistics and their corresponding evaluations among which
p-values. You are probably
Hi Abhishek,
think of your example as being equivalent to putting 1 of sample 1, 10 of
sample 2 and 100 of sample 3 in a dataset and then run your SVM.
This is exactly true for some estimators and approximately true for others,
but always a good intuition.
Hope this helps!
Michael
On Fri, Jul 2
100
> of sample 3, sample 3 will be given a lot of focus during training because
> it exists in majority, but if my dataset size was say 1 million, these
> weights wouldn't really affect much?
>
> Thanks,
> Abhishek
>
> On Jul 28, 2017 10:41 PM, "Michael Eickenbe
Your document says:
> This data has already been pre-processed so that each of the features
and have about the same mean (zero) and variance.
This means that you do this before doing the eigendecomposition.
Check the wikipedia article
https://en.wikipedia.org/wiki/Principal_component_analysis
By the linear nature of the problem the targets are always separately
treated (even if there was a matrix-variate normal prior indicating
covariance between target columns, you could do that adjustment before or
after fitting).
As for different alpha parameters, I think you can specify a different
Hi Jeffrey,
check out these here for neuron data and fmri:
http://crcns.org/
And the ones here for fmri:
https://openfmri.org/
You can get started by installing one of the following packages and using
their dataset downloaders
http://nilearn.github.io/modules/reference.html#module-nilearn.datas
Hi,
that totally depends on the nature of your data and whether the standard
deviation of individual feature axes/columns of your data carry some form
of importance measure. Note that PCA will bias its loadings towards columns
with large standard deviations all else being held equal (meaning that
Hi Lekan,
for which type of estimator are you looking for a batch gradient descent
regressor?
Michael
On Tue, May 29, 2018 at 4:54 PM, Lekan Wahab wrote:
> I have a feeling this question might have been asked before or there's
> some sort of resource somewhere on it but so far I haven't found
You can get one alpha per target in the Ridge estimator (without CV). Then
you would have to code the cv loop yourself.
Depending on how many target you have this can be more efficient than
looping over targets as Alex suggests.
Either way there is some coding to do unfortunately.
Michael
On
Hi Jesse,
I think there was an effort to compare normalization methods on the data
attachment term between Lasso and Ridge regression back in 2012/13, but
this might have not been finished or extended to Logistic Regression.
If it is not documented well, it could definitely benefit from a
documen
What exactly do you mean by "port"? Put already fitted models into a
sklearn estimator object? You can do this as follows:
You should be able to create a `estimator =
sklearn.kernel_ridge.KernelRidge(...)` object, call `fit` to some random
data of the appropriate shape, and then set `estimator.dua
I think it might generate a basis that is capable of generating what you
describe above, but feature expansion concretely reads as
1, a, b, c, a ** 2, ab, ac, b ** 2, bc, c ** 2, a ** 3, a ** 2 * b, a ** 2
* c, a* b ** 2, abc, a*c**2, b**3, b**2 * c, b*c**2, c**3
Hope this helps
On Fri, Nov 22,
Hi,
I think there are many reasons that have led to the current situation.
One is that scikit-learn is based on numpy arrays, which do not offer
categorical data types (yet: ideas are being discussed
https://numpy.org/neps/nep-0041-improved-dtype-support.html Pandas already
has a categorical data
Hi David,
I am assuming you mean that T acts on w.
If T is invertible, you can absorb it into the design matrix by making a
change of variable v=Tw, w=T^-1 v, and use standard ridge regression for v.
If it is not (e.g. when T is a standard finite difference derivative
operator) then this trick won
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