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    El-Hassan Wanas
    @foocraft
    I checked the code, and it seems that it occurs while preparing histograms
    More interestingly, when I set missing to some number, e.g. -9999 for xgboost modeling parameters and pass a dataset that doesn't have missing values, AUC drops to 0.5 from 0.65. This is possibly an unrelated issue, but it seems handling of missing values causes multiple problems
    lesshaste
    @lesshaste
    import xgboost gives the warning cross_validation.py:41: DeprecationWarning: This module was deprecated in version 0.18 in favor of the model_selection module into which all the refactored classes and functions are moved. Also note that the interface of the new CV iterators are different from that of this module. This module will be removed in 0.20.
    but it looks like it should be fixed in rhiever/tpot#284
    was that never merged?
    Dmitry Mottl
    @Mottl
    Hi, everybody! Could someone help me with a xgboost parameters. I have the following code: 
    xgb = XGBRegressor(n_estimators=1000, silent=0)
xgb.fit(train.as_matrix(), trainY, verbose=1, eval_metric="rmse")
P = xgb.predict(test.as_matrix())
    it doesn't output RMSE metric during training. Where am I wrong?
    Ketan Kunde
    @KetanKunde_twitter
    Hi
    i was looking to build xgboost from source
    just wanted to know if anyone on the group has tried doing it before?
    Ketan Kunde
    @KetanKunde_twitter
    also just wanted to confirm whether this is 100% open source
    Guryanov Alexey
    @Goorman
    Hello. Does anyone know if i can slice xgboost's DMatrix by column or block certain features from being used in specific train instance?
    Chris Chow
    @ckchow
    @Goorman it's probably easier to make a new DMatrix with those rows removed or censored in whatever way you need.
    lesshaste
    @lesshaste
    how can you use the pearson correlation coefficient as the loss function with the xgboost regressor?
    Guryanov Alexey
    @Goorman
    @ckchow you have probably meant columns removed and yes this is the only solution i see right now. The problem is that i have to construct DMatrix from sparse libsvm file, and for example to perform greedy feature selection i would have to create new (big) libsvm file every iteration. Which is annoying.
    Chris Chow
    @ckchow
    Oh, I see. can't you construct DMatrices in memory from arrays of arrays?
    Chris Chow
    @ckchow
    At least in Java there is a float[][] constructor, and I think there's a numpy constructor in python as well. might be out of luck if you're using the command line version.
    lesshaste
    @lesshaste
    hi... does anyone understand why xgboost is so slow if you have lots of classes? This code shows the problem https://bpaste.net/show/f7573b5a2fb9 RandomForestClassifier takes about 15 seconds
    but xgboost never terminates at all for me
    Lyndon White
    @oxinabox
    I am training a binary classifier.
    In the problem I am working on,
    I can generate more training data at will.
    In that by running a simulation I can (determenistically) determine the correct label for any feature set
    Each training case takes a bit to generate (say 0.5 seconds).
    The main motivation for training a classifier is that evaluating via simulation takes too long.
    Is there a specific way to task advantage of my capacity to generate more data, that I can do in xgboosting,
    that I couldn't do with say a SVM?
    Its almost an Active Learning problem
    Lyndon White
    @oxinabox
    I'm not sure if there is anything beyond: "Generate more data, both for training and validation , until the validation error hits 0"
    KOLANICH
    @KOLANICH
    Hi everyone! Could anyone explain what are the arguments of a custom loss function?
    objective function
    Data Scientist
    @JayKimBravekjh
    Hi everyone. I joined this room first time today, nice to meet you all
    Asbjørn Nilsen Riseth
    @anriseth
    Is there a built-in way to run XGBoost with a weighted mean square loss function?
    Something like ∑i=1Dwi(yi−y^i)2 \sum_{i=1}^D w_i(y_i-\hat{y}_i)^2
    binyata
    @binyata
    is there a general reason why xgboost predict returns only nan?
    this is for python
    xiaolangyuxin
    @xiaolangyuxin
    multithread
    xgboost predict for multithread works bad
    on windows xp，i found a lots of issues for xgboost，exspacially，
    uaing
    using lolibray
    Peter M. Landwehr
    @pmlandwehr
    Anybody have a changelog for 0.7.post4?
    AbdealiJK
    @AbdealiJK
    Check https://github.com/dmlc/xgboost/releases
    Rydez
    @Rydez
    For XGBoost, when considering time series data, is it worth creating features which represent a change in other features? For example, say I have the feature "total_active_users". Would it make sense to have a feature "change_in_total_active_users"? Or, would that just be redundant?
    Harshal
    @geekyharshal
    Hello people
    Can someone suggest how to begj with xgboost ?
    Harshal
    @geekyharshal
    begin*
    Tommy Yang
    @joyang1
    I use xgboost4j-0.80.jar predictleaf always return 3 leafindex for one label? is this any error?
    @all
    @/all
    Tommy Yang
    @joyang1
    have anyone can answer me?
    Tommy Yang
    @joyang1
    :joy: :joy_cat: :joy:
    Tommy Yang
    @joyang1
    :joy:
    Tommy Yang
    @joyang1
    I used xgboost4j-0.80.jar, xgboost train parameter of round is 800 and train data is 2000000. When I use predictleaf to get leafIndex, the jvm crashed.

    #

    A fatal error has been detected by the Java Runtime Environment:

    #

    SIGSEGV (0xb) at pc=0x00007f42160bf902, pid=880, tid=0x00007f42175f2700

    #

    JRE version: Java(TM) SE Runtime Environment (8.0_171-b11) (build 1.8.0_171-b11)

    Java VM: Java HotSpot(TM) 64-Bit Server VM (25.171-b11 mixed mode linux-amd64 compressed oops)

    Problematic frame:

    V [libjvm.so+0x6d6902] jni_SetFloatArrayRegion+0xc2

    #

    Core dump written. Default location: /data/suzhe/suzhe-1.0-SNAPSHOT/core or core.880

    #

    If you would like to submit a bug report, please visit:

    http://bugreport.java.com/bugreport/crash.jsp

    #

    Java frames: (J=compiled Java code, j=interpreted, Vv=VM code)
    j ml.dmlc.xgboost4j.java.XGBoostJNI.XGBoosterPredict(JJII[[F)I+0
    j ml.dmlc.xgboost4j.java.Booster.predict(Lml/dmlc/xgboost4j/java/DMatrix;ZIZZ)[[F+45
    j ml.dmlc.xgboost4j.java.Booster.predictLeaf(Lml/dmlc/xgboost4j/java/DMatrix;I)[[F+6
    j com.jianshu.suzhe.LRTrainer.train()V+23
    j com.jianshu.suzhe.LRTrainer.main([Ljava/lang/String;)V+30
    v ~StubRoutines::call_stub