Imports graphics, quadprog,randomForest Description Node harvest is a simple interpretable tree-like estimator for high-dimensional regres-sion and classification. A few nodes are selected from an initially large ensem-ble of nodes, each associated with a positive weight. New observations can fall into one or sev-eral nodes and predictions are the weighted average response across all these groups. The pack-age offers visualization of the estimator. Predictions can return the nodes a new observa-tion fell into, along with the mean response of training observations in each node, offering a sim-ple explanation of the prediction
Description For tree ensembles such as random forests, regularized random forests and gradi-ent boos...
These supplementary datasets provide data and code for reproducing key results in the corresponding ...
This is the supplementary data to the article "Large-scale winter catch crop monitoring with Sentine...
Harvesting Trees gathers past and recent results on tree-based methods focalizing the attention on ...
The growing success of Machine Learning (ML) is making significant improvements to predictive models...
A companion working R code and crop yield dataset that illustrate key concepts presented in a scient...
A binary classification model is trained by random forest using data from 41 stations in Norway to p...
A Curated Dataset of 470,925 pull requests for 3349 popular NPM packages, description of the variabl...
Random forests are a statistical learning method widely used in many areas of scientific research es...
Prediction error curves are increasingly used to assess and compare predictions in survival analysis...
This book offers an application-oriented guide to random forests: a statistical learning method exte...
This book offers an application-oriented guide to random forests: a statistical learning method exte...
This work addresses a type of survival prediction (or survival analysis) problem, where the goal is ...
R package for titting non-linear regression models on dependant data with Generalised Least Square (...
R topics documented: cv.tree............................................ 2 deviance.tree...............
Description For tree ensembles such as random forests, regularized random forests and gradi-ent boos...
These supplementary datasets provide data and code for reproducing key results in the corresponding ...
This is the supplementary data to the article "Large-scale winter catch crop monitoring with Sentine...
Harvesting Trees gathers past and recent results on tree-based methods focalizing the attention on ...
The growing success of Machine Learning (ML) is making significant improvements to predictive models...
A companion working R code and crop yield dataset that illustrate key concepts presented in a scient...
A binary classification model is trained by random forest using data from 41 stations in Norway to p...
A Curated Dataset of 470,925 pull requests for 3349 popular NPM packages, description of the variabl...
Random forests are a statistical learning method widely used in many areas of scientific research es...
Prediction error curves are increasingly used to assess and compare predictions in survival analysis...
This book offers an application-oriented guide to random forests: a statistical learning method exte...
This book offers an application-oriented guide to random forests: a statistical learning method exte...
This work addresses a type of survival prediction (or survival analysis) problem, where the goal is ...
R package for titting non-linear regression models on dependant data with Generalised Least Square (...
R topics documented: cv.tree............................................ 2 deviance.tree...............
Description For tree ensembles such as random forests, regularized random forests and gradi-ent boos...
These supplementary datasets provide data and code for reproducing key results in the corresponding ...
This is the supplementary data to the article "Large-scale winter catch crop monitoring with Sentine...