[Scikit-learn-general] What is the relation between the decision function and the predicted probabilities?

[Gil Rutter]

Dear all,

Many classification models in Scikit-learn support the three class methods
predict(), predict_proba() and decision_function(), all taking a features
array X. I can tell by inspection that the predict function returns True
when the decision function is positive. However, what is the connection
between the decision function and the predicted probabilities? How does one
get from one to the other?

If it makes a difference, I am particularly interested in the MLPClassifier
(dev release 0.18) and I am working on multi-class, multi-label problems. I
know that the predicted probabilities arise directly from the activations
of the output layer, but I don't understand the decision function so well.

Best regards,

Gil

[Andreas Mueller]

I suggested to remove the decision_function from MLPClassifier, because
I feel it has little semantics.
It is the output before it goes through a softmax. So
softmax(decision_function) = predict_proba.

Andy

Post by Gil Rutter
Dear all,
Many classification models in Scikit-learn support the three class
methods predict(), predict_proba() and decision_function(), all taking
a features array X. I can tell by inspection that the predict function
returns True when the decision function is positive. However, what is
the connection between the decision function and the predicted
probabilities? How does one get from one to the other?
If it makes a difference, I am particularly interested in the
MLPClassifier (dev release 0.18) and I am working on multi-class,
multi-label problems. I know that the predicted probabilities arise
directly from the activations of the output layer, but I don't
understand the decision function so well.
Best regards,
Gil
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