Man pages for alexanderbrenning/wiml
Interpreting machine-learning models in transformed space
| formula.warped_model | Formula method for warped models |
| gabor | Rock glacier remote sensing using Gabor texture filters |
| inbound.pca_warper | Methods for forward (outbound) and backward (inbound) feature... |
| inbound.rotation_warper | Methods for forward (outbound) and backward (inbound) feature... |
| maipofields | Remote sensing of fruit trees |
| outbound | Generic methods for forward (outbound) and backward (inbound)... |
| pca_warper | Principal components transformation of feature space |
| plot.pca_warper | Plot a PCA warper transformation object |
| pls_warper | Partial least squares transformation of feature space |
| predict.warped_model | Predict from a warped fitted machine-learning model |
| print.summary.pca_warper | Print summary of 'pca_warper' object |
| strucpca_warper | Structured principal component transformation of feature... |
| summary.pca_warper | Summary of a 'pca_warper' object |
| summary.warped_model | Summary of a warped model |
| unwarp | Backtransform from transformed ('warped') to original feature... |
| unwarp.warped_df | Backtransform from transformed ('warped') to original feature... |
| warp | Transform data from feature space into a transformed space |
| warp.data.frame | Transform data from feature space into a transformed space |
| warp_fitted_model | Create a warped view of a fitted machine-learning model |
| warp.formula | Warp model formula |
| warp.function | Create a warped version of a model fitting function |
| wiml | wiml: Interpreting machine-learning models in transformed... |
