fig fig locations

README.Rmd

@@ -16,7 +16,7 @@ knitr::opts_chunk$set(
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 [![R build status](https://github.com/koalaverse/vip/workflows/R-CMD-check/badge.svg)](https://github.com/koalaverse/vip/actions)
-[![Coverage Status](https://img.shields.io/codecov/c/github/koalaverse/vip/master.svg)](https://codecov.io/github/koalaverse/vip?branch=master)
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README.md

@@ -7,7 +7,7 @@
 [![R build
 status](https://github.com/koalaverse/vip/workflows/R-CMD-check/badge.svg)](https://github.com/koalaverse/vip/actions)
 [![Coverage
-Status](https://img.shields.io/codecov/c/github/koalaverse/vip/master.svg)](https://codecov.io/github/koalaverse/vip?branch=master)
+Status](https://img.shields.io/codecov/c/github/koalaverse/vip/master.svg)](https://app.codecov.io/github/koalaverse/vip?branch=master)
 [![R-CMD-check](https://github.com/koalaverse/vip/actions/workflows/R-CMD-check.yaml/badge.svg)](https://github.com/koalaverse/vip/actions/workflows/R-CMD-check.yaml)
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 stable](https://img.shields.io/badge/lifecycle-stable-brightgreen.svg)](https://lifecycle.r-lib.org/articles/stages.html#stable)

inst/references.bib

@@ -332,7 +332,7 @@ @Article{DALEX2018
   volume = {19},
   pages = {1-5},
   number = {84},
-  url = {http://jmlr.org/papers/v19/18-416.html},
+  url = {https://jmlr.org/papers/v19/18-416.html},
 }
 @Book{ggplot22016,
   author = {Hadley Wickham},
@@ -350,7 +350,7 @@ @Article{glmnet2010
   volume = {33},
   number = {1},
   pages = {1--22},
-  url = {http://www.jstatsoft.org/v33/i01/},
+  url = {https://www.jstatsoft.org/v33/i01/},
 }
 @Article{glmnet2011,
   title = {Regularization Paths for Cox's Proportional Hazards Model via Coordinate Descent},
@@ -360,7 +360,7 @@ @Article{glmnet2011
   volume = {39},
   number = {5},
   pages = {1--13},
-  url = {http://www.jstatsoft.org/v39/i05/},
+  url = {https://www.jstatsoft.org/v39/i05/},
 }
 @Misc{iBreakDown2019,
   title = {iBreakDown: Uncertainty of Model Explanations for Non-additive Predictive Models},
@@ -373,7 +373,7 @@ @Article{iml2018
   author = {Christoph Molnar and Bernd Bischl and Giuseppe Casalicchio},
   title = {iml: An R package for Interpretable Machine Learning},
   doi = {10.21105/joss.00786},
-  url = {http://joss.theoj.org/papers/10.21105/joss.00786},
+  url = {https://joss.theoj.org/papers/10.21105/joss.00786},
   year = {2018},
   publisher = {Journal of Open Source Software},
   volume = {3},
@@ -389,7 +389,7 @@ @Article{kernlab2004
   volume = {11},
   number = {9},
   pages = {1--20},
-  url = {http://www.jstatsoft.org/v11/i09/},
+  url = {https://www.jstatsoft.org/v11/i09/},
 }
 @Article{mlr,
   title = {{mlr}: Machine Learning in R},
@@ -399,7 +399,7 @@ @Article{mlr
   volume = {17},
   number = {170},
   pages = {1-5},
-  url = {http://jmlr.org/papers/v17/15-066.html},
+  url = {https://jmlr.org/papers/v17/15-066.html},
 }
 @Article{automatic,
   title = {Automatic model selection for high-dimensional survival analysis},
@@ -456,7 +456,7 @@ @Book{nnet2002
   address = {New York},
   year = {2002},
   note = {ISBN 0-387-95457-0},
-  url = {http://www.stats.ox.ac.uk/pub/MASS4},
+  url = {https://www.stats.ox.ac.uk/pub/MASS4},
 }
 @Article{party2006a,
   title = {Unbiased Recursive Partitioning: A Conditional Inference Framework},
@@ -492,7 +492,7 @@ @Article{party2007a
   year = {2007},
   volume = {8},
   number = {25},
-  url = {http://www.biomedcentral.com/1471-2105/8/25},
+  url = {https://www.biomedcentral.com/1471-2105/8/25},
 }
 @Article{party2008b,
   title = {Conditional Variable Importance for Random Forests},
@@ -501,7 +501,7 @@ @Article{party2008b
   year = {2008},
   volume = {9},
   number = {307},
-  url = {http://www.biomedcentral.com/1471-2105/9/307},
+  url = {https://www.biomedcentral.com/1471-2105/9/307},
 }
 @Article{partykit2015,
   title = {{partykit}: A Modular Toolkit for Recursive Partytioning in {R}},
@@ -510,7 +510,7 @@ @Article{partykit2015
   year = {2015},
   volume = {16},
   pages = {3905-3909},
-  url = {http://jmlr.org/papers/v16/hothorn15a.html},
+  url = {https://jmlr.org/papers/v16/hothorn15a.html},
 }
 @Article{partykit2006,
   title = {Unbiased Recursive Partitioning: A Conditional Inference Framework},
@@ -550,7 +550,7 @@ @Article{plyr2011
   volume = {40},
   number = {1},
   pages = {1--29},
-  url = {http://www.jstatsoft.org/v40/i01/},
+  url = {https://www.jstatsoft.org/v40/i01/},
 }
 @Article{ranger2017,
   title = {{ranger}: A Fast Implementation of Random Forests for High Dimensional Data in {C++} and {R}},
@@ -564,7 +564,7 @@ @Article{ranger2017
 }
 @Article{tree.interpreter2019,
   title = {A {Debiased} {MDI} {Feature} {Importance} {Measure} for {Random} {Forests}},
-  url = {http://arxiv.org/abs/1906.10845},
+  url = {https://arxiv.org/abs/1906.10845},
   abstract = {Tree ensembles such as Random Forests have achieved impressive empirical success across a wide variety of applications. To understand how these models make predictions, people routinely turn to feature importance measures calculated from tree ensembles. It has long been known that Mean Decrease Impurity (MDI), one of the most widely used measures of feature importance, incorrectly assigns high importance to noisy features, leading to systematic bias in feature selection. In this paper, we address the feature selection bias of MDI from both theoretical and methodological perspectives. Based on the original definition of MDI by Breiman et al. for a single tree, we derive a tight non-asymptotic bound on the expected bias of MDI importance of noisy features, showing that deep trees have higher (expected) feature selection bias than shallow ones. However, it is not clear how to reduce the bias of MDI using its existing analytical expression. We derive a new analytical expression for MDI, and based on this new expression, we are able to propose a debiased MDI feature importance measure using out-of-bag samples, called MDI-oob. For both the simulated data and a genomic ChIP dataset, MDI-oob achieves state-of-the-art performance in feature selection from Random Forests for both deep and shallow trees.},
   urldate = {2019-10-18},
   journal = {arXiv:1906.10845 [cs, stat]},
@@ -582,7 +582,7 @@ @incollection{lundberg_unified_2017
   pages = {4765--4774},
   year = {2017},
   publisher = {Curran Associates, Inc.},
-  url = {http://papers.nips.cc/paper/7062-a-unified-approach-to-interpreting-model-predictions.pdf}
+  url = {https://papers.nips.cc/paper/7062-a-unified-approach-to-interpreting-model-predictions.pdf}
 }
 
 @misc{janzing-2019-feature,
@@ -890,7 +890,7 @@ @article{scholbeck-2019-sampling
   journal = {CoRR},
   volume = {abs/1904.03959},
   year = {2019},
-  url = {http://arxiv.org/abs/1904.03959},
+  url = {https://arxiv.org/abs/1904.03959},
   archivePrefix = {arXiv}
 }