Enormous and outstanding addition! Yuriy, I've talked to Akmal and he is happy to help with the documentation. Please start documenting everything and reach out Akmal directly.
-- Denis On Wed, Sep 26, 2018 at 10:31 AM Юрий Бабак <y.ch...@gmail.com> wrote: > Hello Igniters, > > I want to make up some overview of all features and major improvement of ML > module for this release. > > So let me start from the one of our main feature for this release: > > *TensorFlow integration* < > https://issues.apache.org/jira/browse/IGNITE-8670> > > This integration allows us to use Apache Ignite as a data source for > TensorFlow. Also, this integration will allow creating and maintaining > TensorFlow clusters over Apache Ignite and submit TF jobs to those > clusters. More details in the related umbrella ticket. > > Also, for this release we have some new algorithms: > > * Random forest <https://issues.apache.org/jira/browse/IGNITE-8840> > * Gradient boosted trees < > https://issues.apache.org/jira/browse/IGNITE-7149> > * Logistic regression[binary > <https://issues.apache.org/jira/browse/IGNITE-8403>][multi-class > <https://issues.apache.org/jira/browse/IGNITE-8511>] > * ANN <https://issues.apache.org/jira/browse/IGNITE-9261> > > New features related with data preprocessing: > > * Pipeline <https://issues.apache.org/jira/browse/IGNITE-9158> > * L1,L2 normalization <https://issues.apache.org/jira/browse/IGNITE-8663> > * Data filtering for new datasets > <https://issues.apache.org/jira/browse/IGNITE-8666> > * Encoding categorical features [OneHotEncoder > <https://issues.apache.org/jira/browse/IGNITE-8680>][OneOfKEncoder > <https://issues.apache.org/jira/browse/IGNITE-8664>] > * Imputer and Binarizer <https://issues.apache.org/jira/browse/IGNITE-8567 > > > * MaxAbsScaler <https://issues.apache.org/jira/browse/IGNITE-9285> > * Dataset splitting <https://issues.apache.org/jira/browse/IGNITE-8667> > > New features for a model validation: > > * Model estimator <https://issues.apache.org/jira/browse/IGNITE-8669> > * k-fold cross-validation > <https://issues.apache.org/jira/browse/IGNITE-8668> > * Param grid for tuning hyper-parameters in cross-validation > <https://issues.apache.org/jira/browse/IGNITE-8924> > > Other features and improvements: > > * Model updating <https://issues.apache.org/jira/browse/IGNITE-9387> > * ML tutorial <https://issues.apache.org/jira/browse/IGNITE-8741> > * Optional indexing for decision trees > <https://issues.apache.org/jira/browse/IGNITE-9064> > * Learning context for trainers(local parallelizing and logging of training > process) <https://issues.apache.org/jira/browse/IGNITE-8981> > * Unification of API for feature extractor > <https://issues.apache.org/jira/browse/IGNITE-8907> > * Several tickets for removing old unused classes and improvements for code > coverage and examples [1 < > https://issues.apache.org/jira/browse/IGNITE-9124> > ][2 <https://issues.apache.org/jira/browse/IGNITE-9297>][3 > <https://issues.apache.org/jira/browse/IGNITE-9146>][4 > <https://issues.apache.org/jira/browse/IGNITE-9316>][5 > <https://issues.apache.org/jira/browse/IGNITE-9348>][6 > <https://issues.apache.org/jira/browse/IGNITE-8450>] > > Sincerely, > Yuriy Babak >
