A library to train, evaluate, interpret, and productionize decision forest models such as Random Forest and Gradient Boosted Decision Trees.
features=
training argument.model.to_tensorflow_function()
function to convert a YDF model into a
TensorFlow function that can be combined with other TensorFlow operations.
This function is compatible with Keras 2 and Keras 3.servo_api=False
and feed_example_proto=False
for
model.to_tensorflow_function(mode="tf")
to export TensorFlow SavedModel
following respectively the Servo API and consuming serialized TensorFlow
Example protos.pre_processing
and post_processing
arguments to the
model.to_tensorflow_function
function to pack pre/post processing
operations in a TensorFlow SavedModel.may_trigger_gc
on custom losses.sparse_oblique_max_num_projections
.model.plot_tree()
.model.list_compatible_engines()
.model.force_engine(...)
.Doctor Gradus ad Parnassum from "Children's Corner" (L. 113). Claude Debussy
ydf.from_tensorflow_decision_forests()
for importing TF-DF models.Flötenuhren von 1772 und 1793 - Vivace (Hob XIX:13). Joseph Haydn
The commit associated with this release has a typo in its description.
pip install ydf
.//third_party/yggdrasil_decision_forests/learner/distributed_gradient_boosted_trees
to
//third_party/yggdrasil_decision_forests/learner/distributed_gradient_boosted_trees:dgbt
.
Note most case, importing the learners with
//third_party/yggdrasil_decision_forests/learner:all_learners
is
recommended.POISSON
for Poisson log likelihood.