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 >>>> >>>> >>>> >>>> ------------------------------------------------------------------------------ >>>> _______________________________________________ >>>> Scikit-learn-general mailing list >>>> Scikit-learn-general@lists.sourceforge.net >>>> https://lists.sourceforge.net/lists/listinfo/scikit-learn-general >>>> >>>> >>>> >>>> >>>> ------------------------------------------------------------------------------ >>>> >>>> _______________________________________________ >>>> Scikit-learn-general mailing list >>>> Scikit-learn-general@lists.sourceforge.net >>>> https://lists.sourceforge.net/lists/listinfo/scikit-learn-general >>>> >>>> >>> >>> >>> ------------------------------------------------------------------------------ >>> >>> _______________________________________________ >>> Scikit-learn-general mailing list >>> Scikit-learn-general@lists.sourceforge.net >>> https://lists.sourceforge.net/lists/listinfo/scikit-learn-general >>> >>> >> >> >> -- >> ********************************** >> JAGANADH G >> http://jaganadhg.in >> *ILUGCBE* >> http://ilugcbe.org.in >> >> >> ------------------------------------------------------------------------------ >> >> _______________________________________________ >> Scikit-learn-general mailing list >> Scikit-learn-general@lists.sourceforge.net >> https://lists.sourceforge.net/lists/listinfo/scikit-learn-general >> >> >
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