I don’t think you should treat this as an outlier detection problem. Why not try it as a classification problem? The dataset is highly unbalanced. Try
http://scikit-learn.org/stable/modules/generated/sklearn.ensemble.ExtraTreesClassifier.html Use sample_weight to tell the fit method about the class imbalance. But be sure to read up about unbalanced classification and the class_weight parameter to ExtraTreesClassifier. You cannot use the accuracy to find the best model, so read up on model validation in the sklearn User’s Guide. And when you do cross-validation to get the best hyperparameters, be sure you pass the sample weights as well. Time series data is a bit different to use with cross-validation. You may want to add features such as minutes since midnight, day of week, weekday/weekend. And make sure your cross-validation folds respect the time series nature of the problem. http://stackoverflow.com/questions/37583263/scikit-learn-cross-validation-custom-splits-for-time-series-data __________________________________________________________________________________________ Dale Smith | Macy's Systems and Technology | IFS eCommerce | Data Science and Capacity Planning | 5985 State Bridge Road, Johns Creek, GA 30097 | dale.t.sm...@macys.com From: scikit-learn [mailto:scikit-learn-bounces+dale.t.smith=macys....@python.org] On Behalf Of Nicolas Goix Sent: Thursday, August 4, 2016 9:13 PM To: Scikit-learn user and developer mailing list Subject: Re: [scikit-learn] Supervised anomaly detection in time series ⚠ EXT MSG: There are different ways of aggregating estimators. A possibility can be to take the majority vote, or averaging decision functions. On Aug 4, 2016 8:44 PM, "Amita Misra" <amis...@ucsc.edu<mailto:amis...@ucsc.edu>> wrote: If I train multiple algorithms on different subsamples, then how do I get the final classifier that predicts unseen data? I have very few positive samples since it is speed bump detection and we have very few speed bumps in a drive. However, I think that unseen new data would be quite similar to what I have in training data hence if I can correctly learn a classifier for these 5, I hope it should work well for unseen speed bumps. Thanks, Amita On Thu, Aug 4, 2016 at 5:23 PM, Nicolas Goix <goix.nico...@gmail.com<mailto:goix.nico...@gmail.com>> wrote: You can evaluate the accuracy of your hyper-parameters on a few samples. Just don't use the accuracy as your performance measure. For supervised classification, training multiple algorithms on small balanced subsamples usually works well, but 5 anomalies seems indeed to be very little. Nicolas On Aug 4, 2016 7:51 PM, "Amita Misra" <amis...@ucsc.edu<mailto:amis...@ucsc.edu>> wrote: SubSample would remove a lot of information from the negative class. I have more than 500 samples of negative class and just 5 samples of positive class. Amita On Thu, Aug 4, 2016 at 4:43 PM, Nicolas Goix <goix.nico...@gmail.com<mailto:goix.nico...@gmail.com>> wrote: Hi, Yes you can use your labeled data (you will need to sub-sample your normal class to have similar proportion normal-abnormal) to learn your hyper-parameters through CV. You can also try to use supervised classification algorithms on `not too highly unbalanced' sub-samples. Nicolas On Thu, Aug 4, 2016 at 5:17 PM, Amita Misra <amis...@ucsc.edu<mailto:amis...@ucsc.edu>> wrote: Hi, I am currently exploring the problem of speed bump detection using accelerometer time series data. I have extracted some features based on mean, std deviation etc within a time window. Since the dataset is highly skewed ( I have just 5 positive samples for every > 300 samples) I was looking into One ClassSVM covariance.EllipticEnvelope sklearn.ensemble.IsolationForest but I am not sure how to use them. What I get from docs separate the positive examples and train using only negative examples clf.fit(X_train) and then predict the positive examples using clf.predict(X_test) I am not sure what is then the role of positive examples in my training dataset or how can I use them to improve my classifier so that I can predict better on new samples. Can we do something like Cross validation to learn the parameters as in normal binary SVM classification Thanks,? Amita Amita Misra Graduate Student Researcher Natural Language and Dialogue Systems Lab Baskin School of Engineering University of California Santa Cruz -- Amita Misra Graduate Student Researcher Natural Language and Dialogue Systems Lab Baskin School of Engineering University of California Santa Cruz _______________________________________________ scikit-learn mailing list scikit-learn@python.org<mailto:scikit-learn@python.org> https://mail.python.org/mailman/listinfo/scikit-learn _______________________________________________ scikit-learn mailing list scikit-learn@python.org<mailto:scikit-learn@python.org> https://mail.python.org/mailman/listinfo/scikit-learn -- Amita Misra Graduate Student Researcher Natural Language and Dialogue Systems Lab Baskin School of Engineering University of California Santa Cruz _______________________________________________ scikit-learn mailing list scikit-learn@python.org<mailto:scikit-learn@python.org> https://mail.python.org/mailman/listinfo/scikit-learn _______________________________________________ scikit-learn mailing list scikit-learn@python.org<mailto:scikit-learn@python.org> https://mail.python.org/mailman/listinfo/scikit-learn -- Amita Misra Graduate Student Researcher Natural Language and Dialogue Systems Lab Baskin School of Engineering University of California Santa Cruz _______________________________________________ scikit-learn mailing list scikit-learn@python.org<mailto:scikit-learn@python.org> https://mail.python.org/mailman/listinfo/scikit-learn * This is an EXTERNAL EMAIL. 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