In case HTML doesn't work I'll duplicate the message. Hi all,
A machine learning pipeline implemented in https://issues.apache.org/jira/browse/IGNITE-9158 (see discussion here http://apache-ignite-developers.2346864.n4.nabble.com/ML-Machine-Learning-Pipeline-Improvement-tt32772.html) supports hyperparameters variation, but not trainers variation so far. Our reference-framework scikit-learn (according to documentation http://scikit-learn.org/stable/modules/pipeline.html#pipeline) allows to variate trainers and preprocessors the following way: >>> param_grid = dict(reduce_dim=[None, PCA(5), PCA(10)], ... clf=[SVC(), LogisticRegression()], ... clf__C=[0.1, 10, 100]) >>> grid_search = GridSearchCV(pipe, param_grid=param_grid) I think it would be a great improvement for our ML pipeline. Alexey Zinoviev, it would be awesome if you as an author of original ML pipeline take a look at this proposal. -- Sent from: http://apache-ignite-developers.2346864.n4.nabble.com/
