eXtreme Gradient Boosting (GBDT, GBRT or GBM) Library for large-scale and distributed machine learning, on single node, hadoop yarn and more.
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
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!
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?
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?
i was looking to build xgboost from source
just wanted to know if anyone on the group has tried doing it before?
@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.
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
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.
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?
that I couldn't do with say a SVM?
Its almost an Active Learning problem
objective function
this is for python
xgboost predict for multithread works bad
on windows xp，i found a lots of issues for xgboost，exspacially，
uaing
using lolibray
Can someone suggest how to begj with xgboost ?
@all
@/all