boosted_trees_estimators: Boosted Trees Estimator
In tfestimators: Interface to 'TensorFlow' Estimators

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
)

`feature_columns`	An R list containing all of the feature columns used by the model (typically, generated by `feature_columns()`).
`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 `Tensor` objects (typically, these have shape `[batch_size, label_dimension]`).
`weight_column`	A string, or a numeric column created by `column_numeric()` defining feature column representing weights. It is used to down weight or boost examples during training. It will be multiplied by the loss of the example. If it is a string, it is used as a key to fetch weight tensor from the `features` argument. If it is a numeric column, then the raw tensor is fetched by key `weight_column$key`, then `weight_column$normalizer_fn` is applied on it to get weight tensor.
`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 `run_config()`, used to configure the runtime settings.
`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 `label_vocabulary`. If it is not given, that means labels are already encoded as integer or float within `[0, 1]` for `n_classes == 2` and encoded as integer values in `{0, 1,..., n_classes -1}` for `n_classes > 2`. Also there will be errors if vocabulary is not provided and labels are string.