Ok training a OneVsAll classifier it was actually easy.
To inspect the individual classifier I can use the .estimators_ attribute?
Do the estimators in it correspond to the .classes_ that is the
estimators_[0] is trained to recognize .classes_[0] vs the other and so on?
Is there a way to check how the normalization of the data is performed?
Thanks again!
Cheers,
Luca
On 24 September 2013 17:42, Luca Cerone <luca.cer...@gmail.com> wrote:
> Dear all,
>
> I am practising with scikit-learn to solve multiclass classification
> problems.
>
> As an exercise I am trying to build a model to predict the digits dataset
> available with scikit-learn.
>
> Ideally I would like to solve this using logistic regression, building a
> predictor for each digit (one vs all approach).
>
> When a new "digit" comes I predict the output for each of the trained
> classifiers and choose the prediction with the maximum value
> (as you can see I am not doing anything special, I think that it is the
> naivest approach that you can follow).
>
> So far I performed most of this steps manually, but I guess that there
> might be some faster/smarter approach.
>
> For example here is my approach that classifies a digit as 0, 1 or Other.
>
> >>>
> from sklearn.datasets import load_digits
> from sklearn.linear_models import LogisticRegression
>
> digits = load_digits()
> data = digits.data
> target = digits.target
>
> import pylab as pl
> idx = pl.permutation(data.shape[0])
>
> #split the dataset
> n_train_sample = 1000
> idx_train = idx[0:n_train_sample]
> idx_test = idx[0:n_train_sample]
> data_train = data[idx_train, : ]
> target_train = target[idx_train, : ]
> data_test = data[idx_test, : ]
> target_test = target[idx_test,:]
>
> #build the classifier that recognize 0:
> tar_tr_0 = array(map(lambda x : 1 if x == 0 else 0, target_train))
> cfr_0 = LogisticRegression()
> cfr_0.fit(data_train, tar_tr_0)
>
> #build the classifier that recognize 1:
> tar_tr_0 = array(map(lambda x : 1 if x == 1 else 0, target_train))
> cfr_1 = LogisticRegression()
> cfr_1.fit(data_train, tar_tr_1)
>
> #build the classifier that recognizes "other":
> tar_tr_other = array(map(lambda x : 1 if x > 1 else 0, target_train))
> cfr_other = LogisticRegression()
> cfr_other.fit(data_train, tar_tr_other)
> <<<
>
> Next of course there is some code that takes in input the various trained
> classifiers, makes prediction on the test etc etc.
>
> I did this partly for educational purposes (despite I know in theory how
> multiclass classification can be performed I never did the prior steps
> written before,
> which I are useful to learn), partly because I got a bit lost when reading
> the documentation (http://scikit-learn.org/stable/modules/multiclass.html
> ).
>
> For the One versus Rest I think I can
> use sklearn.multiclass.OneVsRestClassifier (and now I am trying to do
> this).
> What I couldn't understand however is how to have access to the internal
> classifiers, to check for their score etc etc.
> I couldn't understand also how to setup a criterion to chose the output.
> What if for example the classifier is very good at discriminating all the
> digits but 4 and 1?
>
> Also I wanted to build a classifier using some form of cross validation,
> but again I got a bit lost.
>
> Sorry if my questions are quite silly!
>
> Thanks a lot in advance for the help!
>
> Cheers,
> Luca
>
> P.s. what if I want to "expand" the list of features to perform logistic
> regression with quadratic terms? Is there an easy way to do this?
>
>
>
>
>
>
--
*Luca Cerone*
Tel: +447585611951
Skype: luca.cerone
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