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Repo info
    Priyanka Goyal
    I think I'll go with phoenix and hbase. thank you so much.
    you're so kind and intelligent
    hi hi, how to make fevalin xgb.train() accept multiple custom eval functions?
    hey guys, does anyone know , how to solve this error , when accesing through xgboost4j on Windows?
    "dmlc-core/include/dmlc/logging.h:235: [10:17:46] src/io/input_split_base.cc:190: file offset not calculated correctly", although the file looks good.
    Mateusz Dymczyk
    hey, been trying (and failing) to implement distributed XGBoost4j using Rabit (like you guys did for Spark and Flink). Do I just need to start a RabitTracker, pass the rt.getWorkerEnvs() to all subnodes, start Rabit.init(env), do train and Rabit.shutdown() on each and just call rt.waitFor(0) on the driver, or is there something more to it? Seems my train instances aren’t communicating with eachother and are just training using local data
    Mateusz Dymczyk
    ok seems it only isn’t working on MacOS, works fine on Linux
    Jonathan Hourany
    Hello! I'm sorry if I missed this information somewhere on Google, but what's the best way to remove xgboost after a global install with sudo python setup.py install? I didn't realize there was a pip installable package already and I'd rather do that in my virtualenv
    El-Hassan Wanas
    @JonathanHourany Could you try sudo pip uninstall xgboost
    Jonathan Hourany
    @foocraft Thanks for the reply. I did, and pip kicked back with package-not-found error
    Sergei Lebedev
    Hi! A question on the JVM API: why are there two overloads for setBaseMargin in Booster? One where the margin is Array[Float] and the other one for the nested case Array[Array[Float]]?
    David Hirvonen
    It looks like v0.6.0 is the last stable release. This is about one year old. I'd like to add an update to the hunter (CMake) package manager (last version was 0.4.0), and am curious if this is the recommended version, if a new release is planned in the near future, or if there is some more recent tested tag/commit that should serve as a stable release point. Thanks!
    El-Hassan Wanas
    Hi all, I'm having an issue since around 2 months now and it's been reported multiple times, #2286 I'm wondering if there's a fundamental reason why this has to happen
    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
    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
    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
    i was looking to build xgboost from source
    just wanted to know if anyone on the group has tried doing it before?
    Ketan Kunde
    also just wanted to confirm whether this is 100% open source
    Guryanov Alexey
    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
    @Goorman it's probably easier to make a new DMatrix with those rows removed or censored in whatever way you need.
    how can you use the pearson correlation coefficient as the loss function with the xgboost regressor?
    Guryanov Alexey
    @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
    Oh, I see. can't you construct DMatrices in memory from arrays of arrays?
    Chris Chow
    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.
    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
    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
    I'm not sure if there is anything beyond: "Generate more data, both for training and validation , until the validation error hits 0"
    Hi everyone! Could anyone explain what are the arguments of a custom loss function?
    objective function
    Data Scientist
    Hi everyone. I joined this room first time today, nice to meet you all
    Asbjørn Nilsen Riseth
    Is there a built-in way to run XGBoost with a weighted mean square loss function?
    Something like i=1Dwi(yiy^i)2 \sum_{i=1}^D w_i(y_i-\hat{y}_i)^2
    is there a general reason why xgboost predict returns only nan?
    this is for python
    xgboost predict for multithread works bad
    on windows xp,i found a lots of issues for xgboost,exspacially,
    using lolibray
    Peter M. Landwehr
    Anybody have a changelog for 0.7.post4?
    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?