README.md

Linear and Logistic Regression

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

All the parameters can be set by param=value

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

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