tf.raw_ops.BoostedTreesUpdateEnsemble

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

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.

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).