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tf.estimator.BoostedTreesClassifier

A Classifier for Tensorflow Boosted Trees models.

Inherits From: Estimator

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.
n_classes number of label classes. Default is binary classification.
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.
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.
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. This is a per instance value. A good default is 1./(n_batches_per_layerbatch_size).
l2_regularization regularization multiplier applied to the square weights of the tree leafs. This is a per instance value. A good default is 1./(n_batches_per_layerbatch_size).
tree_complexity regularization factor to penalize trees with more leaves. This is a per instance value. A good default is 1./(n_batches_per_layer*batch_size).
min_node_weight min_node_weight: minimum hessian a node must have for a split to be considered. This is a per instance value. The value will be compared with