An iterable containing all the feature columns used by
the model. All items in the set should be instances of classes derived
the number of batches to collect statistics per
the Head instance defined for Estimator.
Directory to save model parameters, graph and etc. This can
also be used to load checkpoints from the directory into an estimator to
continue training a previously saved model.
A string or a _NumericColumn created by
tf.feature_column.numeric_column defining feature column representing
weights. It is used to downweight 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. If it is a
_NumericColumn, 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
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).
RunConfig object to configure the runtime settings.
Whether bias centering needs to occur. Bias centering refers
to the first node in the very first tree returning the prediction that
is aligned with the original labels distribution. For example, for
regression problems, the first node will return the mean of the labels.
For binary classification problems, it will return a logit for a prior
probability of label 1.
one of none, pre, post to indicate no pruning, pre-
pruning (do not split a node if not enough gain is observed) and post
pruning (build the tree up to a max depth and then prune branches with
negative gain). For pre and post pruning, you MUST provide
float between 0 and 1. Error bound for quantile
computation. This is only used for float feature columns, and the number
of buckets generated per float feature is 1/quantile_sketch_epsilon.
when wrong arguments are given or unsupported functionalities
Returns the model_fn which is bound to self.params.
Shows the directory name where evaluation metrics are dumped.
Name of the evaluation if user needs to run multiple evaluations on
different data sets, such as on training data vs test data. Metrics for
different evaluations are saved in separate folders, and appear
separately in tensorboard.
A string which is the path of directory contains evaluation metrics.