Yes, I rechecked and the label is correct. As you can see in the code posted, I used the exact same features for all the classifiers so unless rf somehow switches the labels, it should be correct.
I have posted a sample dataset and sample code to reproduce what I'm getting here: https://github.com/pkphlam/spark_rfpredict On Tue, Aug 4, 2015 at 6:42 AM, Yanbo Liang <yblia...@gmail.com> wrote: > It looks like the predicted result just opposite with expectation, so > could you check whether the label is right? > Or could you share several data which can help to reproduce this output? > > 2015-08-03 19:36 GMT+08:00 Barak Gitsis <bar...@similarweb.com>: > >> hi, >> I've run into some poor RF behavior, although not as pronounced as you.. >> would be great to get more insight into this one >> >> Thanks! >> >> On Mon, Aug 3, 2015 at 8:21 AM pkphlam <pkph...@gmail.com> wrote: >> >>> Hi, >>> >>> This might be a long shot, but has anybody run into very poor predictive >>> performance using RandomForest with Mllib? Here is what I'm doing: >>> >>> - Spark 1.4.1 with PySpark >>> - Python 3.4.2 >>> - ~30,000 Tweets of text >>> - 12289 1s and 15956 0s >>> - Whitespace tokenization and then hashing trick for feature selection >>> using >>> 10,000 features >>> - Run RF with 100 trees and maxDepth of 4 and then predict using the >>> features from all the 1s observations. >>> >>> So in theory, I should get predictions of close to 12289 1s (especially >>> if >>> the model overfits). But I'm getting exactly 0 1s, which sounds >>> ludicrous to >>> me and makes me suspect something is wrong with my code or I'm missing >>> something. I notice similar behavior (although not as extreme) if I play >>> around with the settings. But I'm getting normal behavior with other >>> classifiers, so I don't think it's my setup that's the problem. >>> >>> For example: >>> >>> >>> lrm = LogisticRegressionWithSGD.train(lp, iterations=10) >>> >>> logit_predict = lrm.predict(predict_feat) >>> >>> logit_predict.sum() >>> 9077 >>> >>> >>> nb = NaiveBayes.train(lp) >>> >>> nb_predict = nb.predict(predict_feat) >>> >>> nb_predict.sum() >>> 10287.0 >>> >>> >>> rf = RandomForest.trainClassifier(lp, numClasses=2, >>> >>> categoricalFeaturesInfo={}, numTrees=100, seed=422) >>> >>> rf_predict = rf.predict(predict_feat) >>> >>> rf_predict.sum() >>> 0.0 >>> >>> This code was all run back to back so I didn't change anything in >>> between. >>> Does anybody have a possible explanation for this? >>> >>> Thanks! >>> >>> >>> >>> -- >>> View this message in context: >>> http://apache-spark-user-list.1001560.n3.nabble.com/Extremely-poor-predictive-performance-with-RF-in-mllib-tp24112.html >>> Sent from the Apache Spark User List mailing list archive at Nabble.com. >>> >>> --------------------------------------------------------------------- >>> To unsubscribe, e-mail: user-unsubscr...@spark.apache.org >>> For additional commands, e-mail: user-h...@spark.apache.org >>> >>> -- >> *-Barak* >> > > -- Patrick Lam Institute for Quantitative Social Science, Harvard University http://www.patricklam.org
