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)
- Potential Documentation Inaccuracy Regarding Feature Interaction Constraints
- Horizontal Federated Learning with Secure Features RFC
- [bug] Python - Cuda error (without using Cuda) HOT 5
- Pandas 2.2: Index.format is deprecated
- ArrayInterface handler for cuDF DataFrame cannot yet handle Boolean columns HOT 1
- src/metric/auc.cc:322: Check failed: auc <= local_area HOT 1
- XGBoost4j-spark CrossValidation train FAILED on multi-GPU environment: : Multiple processes running on same CUDA device is not supported! HOT 1
- [jvm-packages] Scaladoc is not working in latest XGBoost
- [CI] Tracker for improving build and CI/CD infrastructure
- [CI] Set up a nightly pipeline to test with dev versions of RAPIDS
- xgboost predict takes a long time HOT 1
- xgboost4j_2.12:1.7.6 's (ml/dmlc/xgboost4j/java/XGBoostJNI.XGBoosterPredict) much slower than 0.90 in some model HOT 6
- NumPy 2.0 support HOT 2
- Tutorial on c-api distributed training of xgboost HOT 2
- Python 3.12 `xgboost.core.XGBoostError: Invalid Parameter format for nthread expect int but value='-1'` when `DMatrix` used with `import googlecloudprofiler`. HOT 6
- [CI] Retire Mac Mini worker in BuildKite
- [RFC] New logo for XGBoost HOT 1
- c-ares and BoringSSL version in xgboost 2.0.3 HOT 9
- Federated horizontal result does not align with basic training without federation
- monotone_constraints not working with xgb.regressor (python) HOT 1
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from xgboost.