xgboost/subtree/rabit/rabit-learn/linear

Linear and Logistic Regression

• input format: LibSVM
• Local Example: run-linear.sh
• Runnig on YARN: run-yarn.sh
  • You will need to have YARN
  • Modify ../make/config.mk to set USE_HDFS=1 to compile with HDFS support
  • Run build.sh on ../../yarn on to build yarn jar file

Multi-Threading Optimization

• The code can be multi-threaded, we encourage you to use it
  • Simply add nthread=k where k is the number of threads you want to use
• If you submit with YARN
  • Use --vcores and -mem to request CPU and memory resources
  • Some scheduler in YARN do not honor CPU request, you can request more memory to grab working slots
• Usually multi-threading improves speed in general
  • You can use less workers and assign more resources to each of worker
  • This usually means less communication overhead and faster running time

Parameters

All the parameters can be set by param=value

Important Parameters

• objective [default = logistic]
  • can be linear or logistic
• base_score [default = 0.5]
  • global bias, recommended set to mean value of label
• reg_L1 [default = 0]
  • l1 regularization co-efficient
• reg_L2 [default = 1]
  • l2 regularization co-efficient
• lbfgs_stop_tol [default = 1e-5]
  • relative tolerance level of loss reduction with respect to initial loss
• max_lbfgs_iter [default = 500]
  • maximum number of lbfgs iterations

Optimization Related parameters

• min_lbfgs_iter [default = 5]
  • minimum number of lbfgs iterations
• max_linesearch_iter [default = 100]
  • maximum number of iterations in linesearch
• linesearch_c1 [default = 1e-4]
  • c1 co-efficient in backoff linesearch
• linesarch_backoff [default = 0.5]
  • backoff ratio in linesearch