A Regressor for Tensorflow Boosted Trees models.

Used in the notebooks

Used in the tutorials

feature_columns An iterable containing all the feature columns used by the model. All items in the set should be instances of classes derived from FeatureColumn.
n_batches_per_layer the number of batches to collect statistics per layer. The total number of batches is total number of data divided by batch size.
model_dir Directory to save model parameters, graph and etc. 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.
weight_column A string or a NumericColumn created by tf.fc_old.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.
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 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).