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
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