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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