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I am running a tensor flow model and trying to understand its performance. However, I am not sure about some of the metrics in the results. I have used the Linear classifier using tf.estimator.LinearClassifier. The code and results are attached below:
The model is:
def build_estimator(model_dir, model_type):
wide_columns, deep_columns = build_model_columns()
run_config = tf.estimator.RunConfig().replace(session_config=tf.ConfigProto(device_count='GPU': 0))
if model_type == 'wide':
return tf.estimator.LinearClassifier(
model_dir=model_dir,
feature_columns=wide_columns,
config=run_config)
And the model.evaluate method is:
for n in range(FLAGS.train_epochs // FLAGS.epochs_per_eval):
model.train(input_fn=lambda: input_fn(
FLAGS.train_data, FLAGS.epochs_per_eval, True, FLAGS.batch_size))
results = model.evaluate(input_fn=lambda: input_fn(
FLAGS.test_data, 1, False, FLAGS.batch_size))
I want to know what the metric auc_precision_recall: 0.998951 is in the model.evaluate method. Is it auc or precision or recall or any of the combination of these?
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