In Keras, the first object returned in the score list is loss.
score = model.evaluate(X_test, y_test,verbose=1)
print(score)
[1] [0.025217213829228164, 0.99487179487179489]
Is this the same thing as variance?
$\begingroup$
$\endgroup$
Add a comment
|
1 Answer
$\begingroup$
$\endgroup$
No. Loss measures the error between your predicted values and true values in a given train set. Whereas variance mesures how your model performs (i.e. how the loss changes) on different training sets. Also, variance is an atribute of a given model whereas loss depends on your dataset.
In other words, we can also say that loss on different sets is used to measure variance of a given model.
