Hi Everyone.

I was trying to understand what the confidences score outputted by rasa nlu actually are and how they are computed.

I have been working on intent classification task with tensorflow embedding. Once my model is trained and I parse new/test data, I receive a confidence score along with each probable intent. But I have little to no idea of what actually this confidence score represents.

As mentioned in docs, it does not represent probability. And after some observation of results, it seems to be a one-many type evaluation i.e. for a single text input, I can get multiple intents with high confidence scores.

After having a quick look at the code, I think it is computed in “_tf_sim” function in “embedding_intent_classifier.py” file (Relevant code segment below)

Can somebody please confirm/clarify on how or what confidence score means here?

```
def _tf_sim(self, a, b):
"""Define similarity"""
if self.similarity_type == 'cosine':
a = tf.nn.l2_normalize(a, -1)
b = tf.nn.l2_normalize(b, -1)
if self.similarity_type == 'cosine' or self.similarity_type == 'inner':
sim = tf.reduce_sum(tf.expand_dims(a, 1) * b, -1)
# similarity between intent embeddings
sim_emb = tf.reduce_sum(b[:, 0:1, :] * b[:, 1:, :], -1)
return sim, sim_emb
else:
raise ValueError("Wrong similarity type {}, "
"should be 'cosine' or 'inner'"
"".format(self.similarity_type))
```