Construct a linear model, which can be used to predict a continuous outcome
(in the case of linear_regressor()) or a categorical outcome (in the case
of linear_classifier()).
linear_regressor(feature_columns, model_dir = NULL,
label_dimension = 1L, weight_column = NULL, optimizer = "Ftrl",
config = NULL, partitioner = NULL)linear_classifier(feature_columns, model_dir = NULL, n_classes = 2L,
weight_column = NULL, label_vocabulary = NULL, optimizer = "Ftrl",
config = NULL, partitioner = NULL)
An R list containing all of the feature columns used
by the model (typically, generated by feature_columns()).
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.
Either the name of the optimizer to be used when training the model, or a TensorFlow optimizer instance. Defaults to the FTRL optimizer.
A run configuration created by run_config(), used to configure the runtime
settings.
An optional partitioner for the input layer.
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.
Other canned estimators: boosted_trees_estimators,
dnn_estimators,
dnn_linear_combined_estimators