On 05/06/15 19:03, Andreas Mueller wrote:
> The result of scaled an non-scaled data will be different because the
> regularization will have a different effect.
Oh, so you regularize with lambda*I instead of lambda*diag(Sigma)?
Sturla
---
> Considering the final score, e.g., accuracy, does this mean that with scaling
> and without I will get different results for NB and KNN?
Yes. I think it would really help you to read a little bit about how those
algorithms work -- to develop an intuition how feature scaling affects the
outco
Yes, Andreas. Thank you, just wanted to clarify.
Thank you all for your help and sorry for some silly questions!
Best Regards,
Yury Zhauniarovich
On 5 June 2015 at 20:24, Andreas Mueller wrote:
> Have you read my earlier email explaning just that?
>
> > Tree-based methods are the only ones t
Have you read my earlier email explaning just that?
> Tree-based methods are the only ones that are invariant towards
feature scaling, do DecisionTree*, RandomForest*, ExtraTrees*, Bagging*
(with trees), GradientBoosting* (with trees).
For all other algorithms, the outcome will be different w
Thank you, Sebastian. This is what I want to understand. Considering the
final score, e.g., accuracy, does this mean that with scaling and without I
will get different results for NB and KNN? Or results will be the same like
in case of decision trees?
With gradient descent algorithms it is clear w
"Need" to be scaled sounds a little bit strong ;) -- feature scaling is really
context-dependend. If you are using stochastic gradient descent of gradient
descent you surely want to standardize your data or at least center it for
technical reasons and convergence. However, in naive Bayes, you ju
The result of scaled an non-scaled data will be different because the
regularization will have a different effect.
On 06/05/2015 03:10 AM, Yury Zhauniarovich wrote:
Thank you all! However, what Sturla wrote is now out of my understanding.
One more question. It seems also to me that Naive Bayes
Thank you all! However, what Sturla wrote is now out of my understanding.
One more question. It seems also to me that Naive Bayes classifiers also do
not need data to be scaled. Am I correct?
Best Regards,
Yury Zhauniarovich
On 4 June 2015 at 20:55, Sturla Molden wrote:
> On 04/06/15 20:38, S
On 04/06/15 20:38, Sturla Molden wrote:
> Component-wise EM (aka CEM2) is a better way of avoiding the singularity
> disease, though.
The traditional EM for a GMM proceeds like this:
while True:
global_estep(clusters)
for c in clusters:
mstep(c)
This is inherently unstable. Se
Apropros Mahalanobis distance: To get back to the initial question about scale
invariant classifiers ...
As Andreas already pointed out: The tree-based methods are scale invariant.
But under certain circumstances, you can also add Nearest Neighbor classifiers
and kernel methods such as kernel SV
On 04/06/15 20:18, Andreas Mueller wrote:
> I'm not sure what you mean by that. The cluster-memberships? The means
> and covariances will certainly be different.
We were talking about classification. Yes, memberships. Not means and
covariances, obviously.
-
On 04/06/15 20:18, Andreas Mueller wrote:
> I'm not sure what you mean by that. The cluster-memberships? The means
> and covariances will certainly be different.
The Mahalanobis distance will undo any linear scaling operation.
> They are actually somewhat regularized in scikit-learn, by having a
On 06/04/2015 02:04 PM, Sturla Molden wrote:
> On 04/06/15 17:15, Andreas Mueller wrote:
>
>> Tree-based methods are the only ones that are invariant towards feature
>> scaling, do DecisionTree*, RandomForest*, ExtraTrees*, Bagging* (with
>> trees), GradientBoosting* (with trees).
>>
>> For all o
On 04/06/15 17:15, Andreas Mueller wrote:
> Tree-based methods are the only ones that are invariant towards feature
> scaling, do DecisionTree*, RandomForest*, ExtraTrees*, Bagging* (with
> trees), GradientBoosting* (with trees).
>
> For all other algorithms, the outcome will be different whether
Tree-based methods are the only ones that are invariant towards feature
scaling, do DecisionTree*, RandomForest*, ExtraTrees*, Bagging* (with
trees), GradientBoosting* (with trees).
For all other algorithms, the outcome will be different whether you
scale your data or not.
For algorithms like
Hello everyone,
I have the following question. In general, as a rule of thumb features need
to be scaled using min-max scaling or z-score standardization before being
used in ML algorithms. However, it is not always possible to perform this
procedure (e.g., in cases when you do not have all the da
Thanks.
I didn't thing about **
On 14 July 2014 17:10, Gael Varoquaux wrote:
> > clf2 = svm.SVC(best_para_) #clf2 with best parameters
> > But this does not work.
>
> You could try 'svm.SVC.(**best_params_)'
>
> See http://stackoverflow.com/questions/3394835/args-and-kwargs
>
>
>
> --
> clf2 = svm.SVC(best_para_) #clf2 with best parameters
> But this does not work.
You could try 'svm.SVC.(**best_params_)'
See http://stackoverflow.com/questions/3394835/args-and-kwargs
--
Want fast and easy access t
How to define a classifiers parameters from a dictionary .
using grid search I get the best parameters which is a dictionary.
clf = svm.SVC()
grid_search = GridSearchCV(clf, param_grid=param_grid)
grid_search.fit(X, y)
best_para_ = grid_search.best_params_
# best_para_ is a dictionary which conta
Is there papers that explain how sample weighting is used with each of
those specific classifiers ? In other words, papers explaining how each of
those classifiers is modified to support sample weighting.
2014-06-17 11:31 GMT+02:00 Arnaud Joly :
> Hi,
>
> Without being exhaustive Random forest,
Hi,
Without being exhaustive Random forest, extra trees, bagging, adaboost, naive
bayes and several linear
models support sample weight.
Best regards,
Arnaud
On 17 Jun 2014, at 11:27, Mohamed-Rafik Bouguelia
wrote:
> Hello all,
>
> I've tried to associate weights to instances when trainin
Take a look at the docstring of any classifier and look for
`sample_weight`. If this keyword is provided, you can add sample weights.
Try googling "sklearn sample_weight" or look here
https://github.com/scikit-learn/scikit-learn/search?q=sample_weight for an
overview.
Michael
On Tue, Jun 17, 20
Hello all,
I've tried to associate weights to instances when training an SVM in
sklearn (
http://scikit-learn.org/stable/auto_examples/svm/plot_weighted_samples.html
). Is it possible to use instance weighting with other classifiers from
sklearn (others than the SVM) ? Basically, I know that any
Stackoverflow is better suited for very specific programming questions
rather than large subjective opinion polling (which is actually
explicitly out of the scope of stackoverflow).
So if you can reduce your problem to something reproducible (for
instance in a 10 liner code snippet with expected v
Hi Don.
I suggest you ask your question on stackoverflow with scikit-learn tag.
I think that will get you more feedback.
Also, be sure to include a list of things that you tried and how exactly
you tried them, what the result was, and what the result is that you desire.
Cheers,
Andy
On 09/23/201
Hello,
Perhaps, someone on the list could help me determine which path to take in a
project I am working on.
Here is my problem:
My company manufactures a device that produces two sinewaves in quadrature that
are used to indicate position. The device is called an encoder and is used in
mach
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