* SHAP values for feature contributions
* Fix commenting error
* New polynomial time SHAP value estimation algorithm
* Update API to support SHAP values
* Fix merge conflicts with updates in master
* Correct submodule hashes
* Fix variable sized stack allocation
* Make lint happy
* Add docs
* Fix typo
* Adjust tolerances
* Remove unneeded def
* Fixed cpp test setup
* Updated R API and cleaned up
* Fixed test typo
* [gblinear] add features contribution prediction; fix DumpModel bug
* [gbtree] minor changes to PredContrib
* [R] add feature contribution prediction to R
* [R] bump up version; update NEWS
* [gblinear] fix the base_margin issue; fixes#1969
* [R] list of matrices as output of multiclass feature contributions
* [gblinear] make order of DumpModel coefficients consistent: group index changes the fastest
* 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
* Fix various typos
* Add override to functions that are overridden
gcc gives warnings about functions that are being overridden by not
being marked as oveirridden. This fixes it.
* Use bst_float consistently
Use bst_float for all the variables that involve weight,
leaf value, gradient, hessian, gain, loss_chg, predictions,
base_margin, feature values.
In some cases, when due to additions and so on the value can
take a larger value, double is used.
This ensures that type conversions are minimal and reduces loss of
precision.
* Add format to the params accepted by DumpModel
Currently, only the test format is supported when trying to dump
a model. The plan is to add more such formats like JSON which are
easy to read and/or parse by machines. And to make the interface
for this even more generic to allow other formats to be added.
Hence, we make some modifications to make these function generic
and accept a new parameter "format" which signifies the format of
the dump to be created.
* Fix typos and errors in docs
* plugin: Mention all the register macros available
Document the register macros currently available to the plugin
writers so they know what exactly can be extended using hooks.
* sparce_page_source: Use same arg name in .h and .cc
* gbm: Add JSON dump
The dump_format argument can be used to specify what type
of dump file should be created. Add functionality to dump
gblinear and gbtree into a JSON file.
The JSON file has an array, each item is a JSON object for the tree.
For gblinear:
- The item is the bias and weights vectors
For gbtree:
- The item is the root node. The root node has a attribute "children"
which holds the children nodes. This happens recursively.
* core.py: Add arg dump_format for get_dump()
