Sparsity via BestSparseSplitter
ENH sparse csc matrices indice, indptr and data are 1d
ENH more descriptive name
ENH explicit initialization
ENH deallocate memory
Remove unused varible + clarify some variable name
ENH rename i_ to more conventional p
COSMIT use range instead of xrange
Remove unused varibale
ENH simplify code path
TST imporve tree equality assertion
ENH use appropriate type ofr index
ENH explicit name
ENH extract binary search from code
ENH use memcopy
predict & predict_proba can take sparse data, they are not densyfied internally anymore
Null initialization of feature_to_color and feature_values
Modified the predict and predic_proba function to hanlde sparse input
flake8ed test_tree, and tree.py
Changed random forests to support sparsity