6bd1869026
* Add prediction of feature contributions This implements the idea described at http://blog.datadive.net/interpreting-random-forests/ which tries to give insight in how a prediction is composed of its feature contributions and a bias. * Support multi-class models * Calculate learning_rate per-tree instead of using the one from the first tree * Do not rely on node.base_weight * learning_rate having the same value as the node mean value (aka leaf value, if it were a leaf); instead calculate them (lazily) on-the-fly * Add simple test for contributions feature * Check against param.num_nodes instead of checking for non-zero length * Loop over all roots instead of only the first
This folder contains testcases for xgboost.