I’m trying to load up an ARFF (Weka-style .arff) so I can use it with sklearn’s
classifiers. I found scipy’s arff_load function, but getting it into numpy
arrays of data and targets is nontrivial. Is there a facility for doing this
(semi-)automatically?
I looked through the ./sklearn/datasets
The classifiers have a 'classes_' attribute that contains the original class
labels.
ShNaYkHs ShNaYkHs schrieb:
>Let x an example to classify:
>probas = model_svm.predict_proba([x])[0]
>how can I know what is the label (a string) corresponding to each
>predicted
>probability ? That is, probas
2013/2/28 Robert Layton :
>
> Great, thanks. I have no code online yet, I'm working on a tutorial.
>
> In short, I've been using scikit-learn as a framework, with authorship
> analysis methods implemented directly as ClassifierMixins, rather than using
> pipelines.
> I'll be drafting the presentati
Let x an example to classify:
probas = model_svm.predict_proba([x])[0]
how can I know what is the label (a string) corresponding to each predicted
probability ? That is, probas[i] is the probability of x membership to
which class-label (corresponding to row i) ?
Hi!
I've been performing some tests with KFold cross validation and
encountered a strange behavior:
>>> from sklearn import cross_validation
>>> list(cross_validation.KFold(14, 5, indices=True, shuffle=True,
random_state=32))
[(array([13, 2, 12, 9, 1, 10, 4, 3, 8, 6, 5, 11]), array([0,
7]
sgdclassifier using partial_fit. I want to do naive bayes soon
ShNaYkHs ShNaYkHs schrieb:
>Is there any incremental classifier in sklearn, that can be trained
>incrementally considering one data-point at a time ? An existing one or
>under
>development one ..
>
>
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