Man pages for edahelsinki/slise
Sparse Linear Subset Explanations
| auto_named_list | Creates a named list where the names are taken from the input... |
| data_container | Wrapper for creating a C++ DataContainer that parses... |
| dlog_sigmoid | derivative of log-sigmoid function |
| dsigmoid | derivative of sigmoid function |
| fast_ols | OLS solver that falls back to an optimisation if ncol(X) is... |
| grad_opt_debug | Print debug statement for how the graduated optimisation is... |
| graduated_optimisation | Graduated Optimisation to solve the SLISE problem |
| limited_logit | Computes the logits from probabilities |
| log_approximation_ratio | Calculate the Logarithm of the approximation ratio... |
| log_sigmoid | log-sigmoid function |
| log_sum | Computes log(sum(exp(x))) in a numerically robust way. |
| log_sum_special | Computes log(sum(exp(x) * y)), or log(sum(exp(x))) if all(y... |
| loss_sharp | Sharp Loss Function Exact loss function without gradients |
| loss_sharp_res | Sharp Loss Function Exact loss function without gradients for... |
| loss_smooth | Smooth Loss A loss function for when you want gradients |
| loss_smooth_grad | Smooth Loss Gradient Gradient for the smooth loss function |
| loss_smooth_res | Smooth Loss A loss function for when you want gradients and... |
| matching_epsilon | Find the matching *epsilon |
| next_beta | Find the next beta according to: ¤ approximation_ratio(alpha,... |
| owlqn_c | OWL-QN for optimising loss_smooth Cpp implementation |
| owlqn_r | OWL-QN for optimising loss_smooth R implementation |
| plot.slise | Plot the robust regression or explanation from slise |
| plot.slise_2d | Plot the robust regression or explanation from slise in 2D |
| plot.slise_bar | Plot the robust regression or explanation from slise as bar... |
| plot.slise_distribution | Plot the robust regression or explanation from slise with... |
| plot.slise_mnist | Plot the robust regression or explanation from slise as an... |
| plot.slise_prediction | Plot the robust regression or explanation from slise based on... |
| plot.slise_wordcloud | Plot the robust regression or explanation from slise as a... |
| predict.slise | Predict with a SLISE |
| print.slise | Print the robust regression or explanation from slise |
| scale_identity | A variant of 'scale' that only adds the attributes |
| scale_robust | Robust Scale A variant of 'scale' that is based on median and... |
| sigmoid | sigmoid function |
| simple_pca | Calculate the PCA rotation matrix The implementation is based... |
| slise.explain | SLISE for explaining Black box models. |
| slise.explain_comb | SLISE Black Box Explainer Use SLISE for explaining... |
| slise.explain_find | SLISE Black Box Explainer Use SLISE for explaining... |
| slise.fit | SLISE for robust regression. |
| slise.formula | SLISE for robust regression (using a formula). |
| slise_initialisation_candidates | Initialise the graduated optimisation by sampling candidates |
| slise_initialisation_candidates2 | Initialise the graduated optimisation by sampling candidates |
| slise_initialisation_lasso | Initialise the graduated optimisation with a LASSO solution |
| slise_initialisation_ols | Initialise the graduated optimisation with an "Ordinary Least... |
| slise_initialisation_zeros | Initialise the graduated optimisation with a zero-vector |
| slise.object | Create a result object for SLISE that is similar to other... |
| slise.object_unnormalise | Turn a 'slise.object' result based on normalised data to a... |
| slise-package | slise: Sparse Linear Subset Explanations |
| slise.preprocess | Preprocess the data as necessary before running SLISE |
| sparsity | Sparsity Count the non-zero coefficients |
| unscale | Unscale a scaled matrix / vector |
| which_min_n | Which min n Get the indecies of the n smallest values using... |
