tf.raw_ops.BoostedTreesUpdateEnsemble

Updates the tree ensemble by either adding a layer to the last tree being grown

View aliases

Compat aliases for migration

See Migration guide for more details.

tf.compat.v1.raw_ops.BoostedTreesUpdateEnsemble

tf.raw_ops.BoostedTreesUpdateEnsemble(
    tree_ensemble_handle, feature_ids, node_ids, gains, thresholds,
    left_node_contribs, right_node_contribs, max_depth, learning_rate, pruning_mode,
    name=None
)

or by starting a new tree.

Args
tree_ensemble_handle	A Tensor of type resource. Handle to the ensemble variable.
feature_ids	A Tensor of type int32. Rank 1 tensor with ids for each feature. This is the real id of the feature that will be used in the split.
node_ids	A list of Tensor objects with type int32. List of rank 1 tensors representing the nodes for which this feature has a split.
gains	A list with the same length as node_ids of Tensor objects with type float32. List of rank 1 tensors representing the gains for each of the feature's split.
thresholds	A list with the same length as node_ids of Tensor objects with type int32. List of rank 1 tensors representing the thesholds for each of the feature's split.
left_node_contribs	A list with the same length as node_ids of Tensor objects with type float32. List of rank 2 tensors with left leaf contribs for each of the feature's splits. Will be added to the previous node values to constitute the values of the left nodes.
right_node_contribs	A list with the same length as node_ids of Tensor objects with type float32. List of rank 2 tensors with right leaf contribs for each of the feature's splits. Will be added to the previous node values to constitute the values of the right nodes.
max_depth	A Tensor of type int32. Max depth of the tree to build.
learning_rate	A Tensor of type float32. shrinkage const for each new tree.
pruning_mode	An int that is >= 0. 0-No pruning, 1-Pre-pruning, 2-Post-pruning.
name	A name for the operation (optional).

Returns
The created Operation.