Wrapper on top of liblinear-tools

Linwrap can be used to train a L2-regularized logistic regression classifier or a linear Support Vector Regressor. You can optimize C (the L2 regularization parameter), w (the class weight) or k (the number of bags, i.e. use bagging). You can also find the optimal classification threshold using MCC maximization, use k-folds cross validation, parallelization, etc. In the regression case, you can only optimize C and epsilon.

When using bagging, each model is trained on balanced bootstraps from the training set (one bootstrap for the positive class, one for the negative class). The size of the bootstrap is the size of the smallest (under-represented) class.

usage: linwrap -i <filename>: training set or DB to screen output file C epsilon (for SVR); (0 <= epsilon <= max_i(|y_i|)) w1 gnuplot of bags for bagging (default=off) of cross validation scan for a trained model (requires n>1) also requires (c, w, k) to be known random seed set portion (in [0.0:1.0]) from .AP files (atom pairs; will offset feat. indexes by 1) set (overrides -p) set (overrides -p) set (overrides -p) mode; use trained models mode; save trained models overwriting existing model file for best C scan #steps for SVR ; also, implied by -e and --scan-e weight to counter class imbalance range for w (semantic=start:nsteps:stop) range for e (semantic=start:nsteps:stop) [--c-range <float,float,...>] explicit scan range for C (example='0.01,0.02,0.03') [--k-range <int,int,...>] explicit scan range for k (example='1,2,3,5,10') number of bags (advice: optim. k rather than w)