tf.contrib.learn.LogisticRegressor

Builds a logistic regression Estimator for binary classification.

tf.contrib.learn.LogisticRegressor(
    model_fn, thresholds=None, model_dir=None, config=None,
    feature_engineering_fn=None
)

THIS CLASS IS DEPRECATED. See contrib/learn/README.md for general migration instructions.

This method provides a basic Estimator with some additional metrics for custom binary classification models, including AUC, precision/recall and accuracy.

Example:

# See tf.contrib.learn.Estimator(...) for details on model_fn structure
def my_model_fn(...):
  pass

estimator = LogisticRegressor(model_fn=my_model_fn)

# Input builders
def input_fn_train:
  pass

estimator.fit(input_fn=input_fn_train)
estimator.predict(x=x)

Args
model_fn	Model function with the signature: (features, labels, mode) -> (predictions, loss, train_op). Expects the returned predictions to be probabilities in [0.0, 1.0].
thresholds	List of floating point thresholds to use for accuracy, precision, and recall metrics. If None, defaults to [0.5].
model_dir	Directory to save model parameters, graphs, etc. This can also be used to load checkpoints from the directory into a estimator to continue training a previously saved model.
config	A RunConfig configuration object.
feature_engineering_fn	Feature engineering function. Takes features and labels which are the output of input_fn and returns features and labels which will be fed into the model.

Returns
An Estimator instance.