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/ <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 > > <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 list >> Scikit-learn-general@lists.sourceforge.net >> <mailto:Scikit-learn-general@lists.sourceforge.net> >> https://lists.sourceforge.net/lists/listinfo/scikit-learn-general >> <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
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