RFE/RFECV are not only feature selectors (SelectorMixin) but also classifiers/regressors (MetaEstimatorMixin), though ELI5 explain_weights doesn't support them as classifiers/regressors. The final fit of an RFE/RFECV object is a fitted estimator with either rfe.estimator_.coef_ or rfe.estimator_.feature_importances_ and in sklearn you do not usually follow up RFE/RFECV with another classifier

Hi,

The documentation states that "By using this class it is possible to save some time if the same input sequence is passed to trainers/taggers more than once"; however I am struggling to find any examples of it being used. What is the recommended approach to train and predict with ItemSequence objects?

Many thanks

Without scikit-learn and seqlearn it will be possible to install webstruct without numpy/scipy stack and without Cython. They are used for auxilary things: nicer __repr__s, Pipeline instead of hand-written classes, metrics (which are broken anyways, see scrapinghub/webstruct#14) - that's nothing serious or hard to replace.

Please create examples to see how to use CRFSuite in nodejs

• Train a dataset
• Tag a test sample