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)
Arguments
feature_columns  An R list containing all of the feature columns used
by the model (typically, generated by 
n_batches_per_layer  The number of batches to collect statistics per layer. 
model_dir  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. 
label_dimension  Number of regression targets per example. This is the
size of the last dimension of the labels and logits 
weight_column  A string, or a numeric column created by

n_trees  Number trees to be created. 
max_depth  Maximum depth of the tree to grow. 
learning_rate  Shrinkage parameter to be used when a tree added to the model. 
l1_regularization  Regularization multiplier applied to the absolute weights of the tree leafs. 
l2_regularization  Regularization multiplier applied to the square weights of the tree leafs. 
tree_complexity  Regularization factor to penalize trees with more leaves. 
min_node_weight  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). 
config  A run configuration created by 
n_classes  The number of label classes. 
label_vocabulary  A list of strings represents possible label values.
If given, labels must be string type and have any value in

See also
Other canned estimators: dnn_estimators
,
dnn_linear_combined_estimators
,
linear_estimators