An R list containing all of the feature columns used by the model (typically, generated by feature_columns()).

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 Tensor objects (typically, these have shape [batch_size, label_dimension]).

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.

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 run_config(), used to configure the runtime settings.

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 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.