Hey,
the mlxtend library worked great on my Computer.
Now installed it on an server.
import mlxtend works fine
but if i want to import the EnsembleClassifier he gives ma an error like:
from mlxtend.sklearn import EnsembleClassifier :
"No module named sklearn"
import sklearn works also.
Does anyone knows why? I installed mlxtend with "python setup.py install"
I think, it is version 0.28
On 2 June 2015 at 17:41, Sebastian Raschka <se.rasc...@gmail.com> wrote:
> Hi, Herbert,
> I can't help you with the accuracy problem since this can be due to many
> different things. However, there is now a way to combine different
> classifiers for majority rule voting, the sklearn.ensemble.VotingClassifier
> (. It is not in the current stable release yet but you could get it from
> the scikit-learn dev version from github.
>
> Alternatively, if you don't want to install the scikit-learn dev version,
> you could use the EnsembleClassifier from mlxtend until the next stable
> release of scikit-learn -- slightly different syntax but the same principle
> http://rasbt.github.io/mlxtend/docs/sklearn/ensemble_classifier/ (this is
> basically the original implementation that was later ported to
> scikit-learn).
>
> Hope that helps.
>
> Best,
> Sebastian
>
>
> On Jun 2, 2015, at 11:25 AM, Herbert Schulz <hrbrt....@gmail.com> wrote:
>
> Thanks that helped.
>
> But i just can't get an higher accuracy then 45%... don't now why. also
> with logicstic regression and so on..
>
> Is there a way to combine for example an SVM with a decision tree?
>
> Herb
>
> On 2 June 2015 at 11:19, Michael Eickenberg <michael.eickenb...@gmail.com>
> wrote:
>
>> Some configurations are not implemented or difficult to evaluate in the
>> dual. Setting dual=True/False doesn't change the result, so please don't
>> vary it as you would vary other parameters. It can however sometimes yield
>> a speed-up. Here you should try setting dual=False as a first means of
>> debugging.
>>
>> Michael
>>
>> On Tue, Jun 2, 2015 at 11:04 AM, Herbert Schulz <hrbrt....@gmail.com>
>> wrote:
>>
>>> Does anyone know why this failure occurs?
>>>
>>> ValueError: Unsupported set of arguments: loss='l1' and
>>> penalty='squared_hinge'are not supported when dual=True, Parameters:
>>> penalty='l1', loss='squared_hinge', dual=True
>>>
>>> I'm using a Linear SVC ( in andreas example code).
>>>
>>>
>>> On 1 June 2015 at 13:38, Herbert Schulz <hrbrt....@gmail.com> wrote:
>>>
>>>> Cool, thx for that!
>>>>
>>>>
>>>> Herb
>>>>
>>>> On 1 June 2015 at 12:16, JAGANADH G <jagana...@gmail.com> wrote:
>>>>
>>>>> Hi
>>>>>
>>>>> I have listed sklearn feature selection with minimal examples here
>>>>>
>>>>>
>>>>> http://nbviewer.ipython.org/github/jaganadhg/data_science_notebooks/blob/master/sklearn/scikit_learn_feature_selection.ipynb
>>>>>
>>>>> Jagan
>>>>>
>>>>> On Thu, May 28, 2015 at 10:14 PM, Herbert Schulz <hrbrt....@gmail.com>
>>>>> wrote:
>>>>>
>>>>>> Thank's to both of you!!! I realy appreciate it! I will try
>>>>>> everything this weekend.
>>>>>>
>>>>>> Best regards,
>>>>>>
>>>>>> Herb
>>>>>>
>>>>>> On 28 May 2015 at 18:21, Sebastian Raschka <se.rasc...@gmail.com>
>>>>>> wrote:
>>>>>>
>>>>>>> I agree with Andreas,
>>>>>>> typically, a large number of features also shouldn't be a big
>>>>>>> problem for random forests in my experience; however, it of course
>>>>>>> depends
>>>>>>> on the number of trees and training samples.
>>>>>>>
>>>>>>> If you suspect that overfitting might be a problem using
>>>>>>> unregularized classifiers, also consider "dimensionality
>>>>>>> reduction"/"feature exctraction" techniques to compress the feature
>>>>>>> space,
>>>>>>> e.g., linear or kernel PCA, or other methods listed in the manifold
>>>>>>> learning section on the scikit-website.
>>>>>>>
>>>>>>> However, there are scenarios where you'd want to keep the "original"
>>>>>>> features (in contrast to e.g., principal components), and there are
>>>>>>> scenarios where linear methods such as LinearSVC(penalty='l1') may not
>>>>>>> work
>>>>>>> so well (e.g., for non-linear problems). The optimal solution would be
>>>>>>> to
>>>>>>> exhaustively test all feature combinations to see which works best,
>>>>>>> however, this can be quite costly. For demonstration purposes, I
>>>>>>> implemented "sequential backward selection" (
>>>>>>> http://rasbt.github.io/mlxtend/docs/sklearn/sequential_backward_selection/)
>>>>>>> some time ago; a simple greedy alternative to the exhaustive search,
>>>>>>> maybe
>>>>>>> you are lucky and it works well in your case? . When I find time after
>>>>>>> my
>>>>>>> summer projects, I am planning to implement some genetic algos for
>>>>>>> feature
>>>>>>> selection...
>>>>>>>
>>>>>>> Best,
>>>>>>> Sebastian
>>>>>>>
>>>>>>>
>>>>>>> On May 28, 2015, at 11:59 AM, Andreas Mueller <t3k...@gmail.com>
>>>>>>> wrote:
>>>>>>>
>>>>>>> Hi Herbert.
>>>>>>> 1) Often reducing the features space does not help with accuracy,
>>>>>>> and using a regularized classifier leads to better results.
>>>>>>> 2) To do feature selection, you need two methods: one to reduce the
>>>>>>> set of features, another that does the actual supervised task
>>>>>>> (classification here).
>>>>>>>
>>>>>>> Have you tried just using the standard classifiers? Clearly you
>>>>>>> tried the RF, but I'd also try a linear method like
>>>>>>> LinearSVC/LogisticRegression or a kernel SVC.
>>>>>>>
>>>>>>> If you want to do feature selection, what you need to do is
>>>>>>> something like this:
>>>>>>>
>>>>>>> feature_selector = LinearSVC(penalty='l1') #or maybe start with
>>>>>>> SelectKBest()
>>>>>>> feature_selector.train(X_train, y_train)
>>>>>>>
>>>>>>> X_train_reduced = feature_selector.transform(X_train)
>>>>>>> X_test_reduced = feature_selector.transform(X_test)
>>>>>>>
>>>>>>> classifier = RandomForestClassifier().fit(X_train_reduced, y_train)
>>>>>>>
>>>>>>> prediction = classifier.predict(X_test_reduced)
>>>>>>>
>>>>>>>
>>>>>>> Or you use a pipeline, as here:
>>>>>>> http://scikit-learn.org/dev/auto_examples/feature_selection/feature_selection_pipeline.html
>>>>>>> Maybe we should add a version without the pipeline to the examples?
>>>>>>>
>>>>>>> Cheers,
>>>>>>> Andy
>>>>>>>
>>>>>>>
>>>>>>>
>>>>>>> On 05/28/2015 08:32 AM, Herbert Schulz wrote:
>>>>>>>
>>>>>>> Hello,
>>>>>>> I'm using scikit-learn for machine learning.
>>>>>>> I have 800 samples with 2048 features, therefore i want to reduce my
>>>>>>> features to get hopefully a better accuracy.
>>>>>>>
>>>>>>> It is a multiclass problem (class 0-5), and the features consists of
>>>>>>> 1's and 0's: [1,0,0,0,1,1,1,1,1,0,0,0,0,0,0,0,0....,0]
>>>>>>>
>>>>>>> I'm using the Randfom Forest Classifier.
>>>>>>>
>>>>>>> Should i just feature select the training data ? And is it enough if
>>>>>>> I'm using this code:
>>>>>>>
>>>>>>> X_train, X_test, y_train, y_test = train_test_split(X, y,
>>>>>>> test_size=.3)
>>>>>>>
>>>>>>>
>>>>>>> clf=RandomForestClassifier(n_estimators=200,warm_start=True,criterion='gini',
>>>>>>> max_depth=13)
>>>>>>> clf.fit(X_train, y_train).transform(X_train)
>>>>>>>
>>>>>>> predicted=clf.predict(X_test)
>>>>>>> expected=y_test
>>>>>>> confusionMatrix=metrics.confusion_matrix(expected,predicted)
>>>>>>>
>>>>>>> Cause the accuracy didn't get higher. Is everything ok in the code
>>>>>>> or am I doing something wrong?
>>>>>>>
>>>>>>> I'll be very grateful for your help.
>>>>>>>
>>>>>>>
>>>>>>>
>>>>>>>
>>>>>>>
>>>>>>> ------------------------------------------------------------------------------
>>>>>>>
>>>>>>>
>>>>>>>
>>>>>>> _______________________________________________
>>>>>>> Scikit-learn-general mailing
>>>>>>> listScikit-learn-general@lists.sourceforge.nethttps://lists.sourceforge.net/lists/listinfo/scikit-learn-general
>>>>>>>
>>>>>>>
>>>>>>>
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>>>>>>>
>>>>>>>
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>>>>>>
>>>>>>
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>>>>>>
>>>>>>
>>>>>
>>>>>
>>>>> --
>>>>> **********************************
>>>>> JAGANADH G
>>>>> http://jaganadhg.in
>>>>> *ILUGCBE*
>>>>> http://ilugcbe.org.in
>>>>>
>>>>>
>>>>> ------------------------------------------------------------------------------
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>>>>
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