* Implement CFS/FCBF in splitter
* Split Splitter class to its own file
Update hyperparams table in docs
Implement CFS/FCBS with max_features and variable type
* Set mfs to continuous variables
* Fix some tests and style issues in Splitter
* Update requirements in github CI
* Add first doc info to sources
* Update doc to separate classes in api
* Refactor build_predictor
* Fix random_sate issue in non linear kernels
* Refactor score method using base class implementation
* Some quality refactoring
* Fix codecov config.
* Add sigmoid kernel
* Refactor setup and add Makefile
* Add Hyperparameters description to README
Comment get_subspace method
Add environment info for binder (runtime.txt)
* Complete source comments
Change docstring type to numpy
update hyperameters table and explanation
* Fix problem with zero weighted samples
Solve WARNING: class label x specified in weight is not found
with a different approach
* Allow update of scikitlearn to latest version
#15 Create impurity function in Stree (consistent name, same criteria as other splitter parameter)
Create test for the new function
Update init test
Update test splitter parameters
Rename old impurity function to partition_impurity
close#15
* Complete implementation of splitter_type = impurity with tests
Remove max_distance & min_distance splitter types
* Fix mistake in computing multiclass node belief
Set default criterion for split to entropy instead of gini
Set default max_iter to 1e5 instead of 1e3
change up-down criterion to match SVC multiclass
Fix impurity method of splitting nodes
Update jupyter Notebooks
The split criteria functions min and max distance return classes while
max_samples return distances positives and negatives to hyperplane of
the class with more samples in node
Removed (by now) predict_proba. Created a notebook in jupyter
Added split_criteria parameter with min_distance and max_samples values
Refactor _distances
Refactor _split_criteria
Refactor _reorder_results
