Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Hyperlink DOIs against preferred resolver #80

Open
wants to merge 1 commit into
base: master
Choose a base branch
from
Open

Hyperlink DOIs against preferred resolver #80

Show file tree
Hide file tree
Changes from all commits
Commits
Show all changes
1 commit
Select commit
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
2 changes: 1 addition & 1 deletion tutorials/ensembles-stacking/README.md
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -48,7 +48,7 @@ Stacking is a broad class of algorithms that involves training a second-level "m
### Some Background
[Leo Breiman](https://en.wikipedia.org/wiki/Leo_Breiman), known for his work on classification and regression trees and the creator of the Random Forest algorithm, formalized stacking in his 1996 paper, ["Stacked Regressions"](http://statistics.berkeley.edu/sites/default/files/tech-reports/367.pdf). Although the idea originated with [David Wolpert](https://en.wikipedia.org/wiki/David_Wolpert) in 1992 under the name ["Stacked Generalization"](http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.56.1533), the modern form of stacking that uses internal k-fold cross-validation was Dr. Breiman's contribution.

However, it wasn't until 2007 that the theoretical background for stacking was developed, which is when the algorithm took on the name, "Super Learner". Until this time, the mathematical reasons for why stacking worked were unknown and stacking was considered a "black art." The Super Learner algorithm learns the optimal combination of the base learner fits. In an article titled, ["Super Learner"](http://dx.doi.org/10.2202/1544-6115.1309), by [Mark van der Laan](http://www.stat.berkeley.edu/~laan/Laan/laan.html) et al., proved that the Super Learner ensemble represents an asymptotically optimal system for learning.
However, it wasn't until 2007 that the theoretical background for stacking was developed, which is when the algorithm took on the name, "Super Learner". Until this time, the mathematical reasons for why stacking worked were unknown and stacking was considered a "black art." The Super Learner algorithm learns the optimal combination of the base learner fits. In an article titled, ["Super Learner"](https://doi.org/10.2202/1544-6115.1309), by [Mark van der Laan](http://www.stat.berkeley.edu/~laan/Laan/laan.html) et al., proved that the Super Learner ensemble represents an asymptotically optimal system for learning.


### Super Learner Algorithm
Expand Down
2 changes: 1 addition & 1 deletion tutorials/ensembles-stacking/ensembles-stacking.R
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -44,7 +44,7 @@
#### Some Background
#[Leo Breiman](https://en.wikipedia.org/wiki/Leo_Breiman), known for his work on classification and regression trees and the creator of the Random Forest algorithm, formalized stacking in his 1996 paper, ["Stacked Regressions"](http://statistics.berkeley.edu/sites/default/files/tech-reports/367.pdf). Although the idea originated with [David Wolpert](https://en.wikipedia.org/wiki/David_Wolpert) in 1992 under the name ["Stacked Generalization"](http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.56.1533), the modern form of stacking that uses internal k-fold cross-validation was Dr. Breiman's contribution.
#
#However, it wasn't until 2007 that the theoretical background for stacking was developed, which is when the algorithm took on the name, "Super Learner". Until this time, the mathematical reasons for why stacking worked were unknown and stacking was considered a "black art." The Super Learner algorithm learns the optimal combination of the base learner fits. In an article titled, ["Super Learner"](http://dx.doi.org/10.2202/1544-6115.1309), by [Mark van der Laan](http://www.stat.berkeley.edu/~laan/Laan/laan.html) et al., proved that the Super Learner ensemble represents an asymptotically optimal system for learning.
#However, it wasn't until 2007 that the theoretical background for stacking was developed, which is when the algorithm took on the name, "Super Learner". Until this time, the mathematical reasons for why stacking worked were unknown and stacking was considered a "black art." The Super Learner algorithm learns the optimal combination of the base learner fits. In an article titled, ["Super Learner"](https://doi.org/10.2202/1544-6115.1309), by [Mark van der Laan](http://www.stat.berkeley.edu/~laan/Laan/laan.html) et al., proved that the Super Learner ensemble represents an asymptotically optimal system for learning.
#
#
#### Super Learner Algorithm
Expand Down