Boosted Trees Estimator
Construct a boosted trees estimator.
boosted_trees_regressor(feature_columns, n_batches_per_layer, model_dir = NULL, label_dimension = 1L, weight_column = NULL, n_trees = 100L, max_depth = 6L, learning_rate = 0.1, l1_regularization = 0, l2_regularization = 0, tree_complexity = 0, min_node_weight = 0, config = NULL) boosted_trees_classifier(feature_columns, n_batches_per_layer, model_dir = NULL, n_classes = 2L, weight_column = NULL, label_vocabulary = NULL, n_trees = 100L, max_depth = 6L, learning_rate = 0.1, l1_regularization = 0, l2_regularization = 0, tree_complexity = 0, min_node_weight = 0, config = NULL)
An R list containing all of the feature columns used
by the model (typically, generated by
The number of batches to collect statistics per layer.
Directory to save the model parameters, graph, and so on. This can also be used to load checkpoints from the directory into a estimator to continue training a previously saved model.
Number of regression targets per example. This is the
size of the last dimension of the labels and logits
A string, or a numeric column created by
Number trees to be created.
Maximum depth of the tree to grow.
Shrinkage parameter to be used when a tree added to the model.
Regularization multiplier applied to the absolute weights of the tree leafs.
Regularization multiplier applied to the square weights of the tree leafs.
Regularization factor to penalize trees with more leaves.
Minimum hessian a node must have for a split to be considered. The value will be compared with sum(leaf_hessian)/(batch_size * n_batches_per_layer).
A run configuration created by
The number of label classes.
A list of strings represents possible label values.
If given, labels must be string type and have any value in