Description Usage Arguments See Also
Construct a boosted trees estimator.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32  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
)

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

Other canned estimators:
dnn_estimators
,
dnn_linear_combined_estimators
,
linear_estimators
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