Hi Jeff,
In general, most implementations of predict_proba are some proxy the
conditional probability p(y|x). Some of them really are modelling this
quantity quite well (e.g., gaussian process) while for some others it
is closer to a heuristic than to the actual p(y|x) (e.g., with linear
models).
On 12/01/2015 11:28 PM, Jeff Levesque wrote:
> Is there a way to determine if the data used with the SVC class, used to
> generate an SVM model, would generate a poor model, or confidence percentage
> (or 'decision_function', if that's preferred)?
>
>
I don't understand the question.
I don't understand the question.
By definition this function provides probability estimates. In the case
of SVC, it is possible that these probabilities don't coincide with
the prediction.
You could make predictions using the probabilities if you'd liked. There
is no other way to ensure
Is there a way to determine if the data used with the SVC class, used to
generate an SVM model, would generate a poor model, or confidence percentage
(or 'decision_function', if that's preferred)?
Jeffrey Levesque
https://github.com/jeff1evesque/
(603) 969-5363
Sent from my iPhone
> On Dec
Hey all,
I have a specific question: how do I ensure that the '.predict_proba()' method,
associated with the classification sklearn, accurately provides probability,
that a provided value is one of the predefined class:
