Comments (1)
Feature engineering is very dataset dependent and hard to be summarized into an unique approach. Usually everyone have their own way. I do prefer to have benchmarks for specific dataset in https://github.com/tqchen/xgboost/tree/master/demo
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Related Issues (20)
- Defining a callback to write hessians of train observations to a csv file HOT 2
- Slow inference on sphoradic stremaing data HOT 2
- XGBoost GPU Warning When Working with BayesSearchCV (XGBoost is running on: cuda:0, while the input data is on: cpu.)
- help installing xgboost with gpu HOT 2
- 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
- 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
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