Comments (7)
Hello. There is good course such as Linux 101 in edx.org. You can get Linux knowledge from there. To import xgboost, please refer demo here
from xgboost.
You will need to add path of xgboost wrapper to environment variable. See
# append the path to xgboost, you may need to change the following line
# alternatively, you can add the path to PYTHONPATH environment variable
sys.path.append('../../wrapper')
import xgboost as xgb
from xgboost.
Thanks a lot!
from xgboost.
I fixed problem. Now I can run sample code.
This xgboost is GREAT!!!
from xgboost.
thank you for using the xgboost
from xgboost.
How can I use it only by c++?
from xgboost.
check out instruction in the https://github.com/dmlc/xgboost/blob/master/doc/README.md if you mean CLI version. If you mean use the c++ class, you will need to read the interface of modules in xgboost
from xgboost.
Related Issues (20)
- Invalid classes inferred from unique values of `y`. HOT 3
- RROR ml.dmlc.xgboost4j.java.RabitTracker:[AutoML] Uncaught exception thrown by worker: java.lang.InterruptedException: null HOT 1
- Start from previous boosting rounds for training continuation.
- CMake R package build fails HOT 1
- [Bug] [Python] Categorical CUDA fails on a data validation check if there's a float column containing only NaNs HOT 1
- Issue with Feature Importance Visualization due to Special Character Handling in Booster.getScore with Xgboost4j v1.7.5 HOT 2
- support sequence interface like lightgbm.Sequence HOT 5
- DMatrix handling of one-hot labels (Python) HOT 4
- Double checking min_split_loss in bag and out of bag HOT 5
- default value of `lambdarank_pair_method` = `topk` in ranking parameters not the `mean` HOT 3
- `rank:pairwise` conflict with `ndcg_exp_gain` = `False` HOT 5
- An error occurred: jemalloc unsupported system page size HOT 2
- Make external CUDA stream wait.
- Docs on params and Python tutorial for regression HOT 1
- Docs for custom objectives could cover more details HOT 9
- Which one is `y_true` when I use a custom loss for regression mode. HOT 3
- clarification needed for model/saving loading HOT 2
- Missing XGBoostRanker in xgboost4j-spark jvm package HOT 3
- multi label support in Scala xgboost. HOT 8
- SparkXGBClassifier does not validate params HOT 6
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from xgboost.