--sparse

Switch

--sparse

Description

Use sparse representation for X when training / testing.

Argument and Default Value

Default value is False.

Details

Often calls the Scipy csr_matrix (Compressed Sparse Row) class. Sparse matrices can be used in arithmetic operations: they support addition, subtraction, multiplication, division, and matrix power.

Advantages of the CSR format efficient arithmetic operations CSR + CSR, CSR * CSR, etc. efficient row slicing fast matrix vector products Disadvantages of the CSR format slow column slicing operations changes to the sparsity structure are expensive

Other Switches

Optional Switches: --train_regression, fwflag_train_reg --test_regression --combo_test_regression, fwflag_combo_test_reg --control_adjust_outcomes_regression, fwflag_control_adjust_reg? fwflag_test_combined_regression? --predict_regression, fwflag_predict_reg --predict_regression_to_feats fwflag_predict_cv_to_feats, fwflag_predict_combo_to_feats, fwflag_predict_regression_all_to_feats? --train_classifiers, fwflag_train_class --test_classifiers --combo_test_classifiers --predict_classifiers, fwflag_predict_class --roc --predict_classifiers_to_feats fwflag_predict_cv_to_feats, fwflag_predict_combo_to_feats, fwflag_predict_regression_all_to_feats? fwflag_train_c2r? fwflag_test_c2r? fwflag_predict_c2r? --fit_reducer Example Commands ================ .. code:doc:fwflag_block:: python