| /dports/misc/py-xgboost/xgboost-1.5.1/R-package/man/ |
| H A D | xgb.plot.importance.Rd | 2 % Please edit documentation in R/xgb.ggplot.R, R/xgb.plot.importance.R
3 \name{xgb.ggplot.importance}
4 \alias{xgb.ggplot.importance}
5 \alias{xgb.plot.importance}
8 xgb.ggplot.importance(
17 xgb.plot.importance(
24 plot = TRUE,
33 \item{measure}{the name of importance measure to plot.
53 The \code{xgb.plot.importance} function creates a \code{barplot} (when \code{plot=TRUE})
61 \code{xgb.plot.importance} uses base R graphics, while \code{xgb.ggplot.importance} uses the ggplot…
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| H A D | xgb.plot.shap.summary.Rd | 2 % Please edit documentation in R/xgb.ggplot.R, R/xgb.plot.shap.R
3 \name{xgb.ggplot.shap.summary}
4 \alias{xgb.ggplot.shap.summary}
5 \alias{xgb.plot.shap.summary}
6 \title{SHAP contribution dependency summary plot}
8 xgb.ggplot.shap.summary(
20 xgb.plot.shap.summary(
39 feature importance is calculated, and \code{top_n} high ranked features are taken.}
46 \item{trees}{passed to \code{\link{xgb.importance}} when \code{features = NULL}.}
73 # See \code{\link{xgb.plot.shap}}.
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| H A D | xgb.shap.data.Rd | 2 % Please edit documentation in R/xgb.plot.shap.R
3 \name{xgb.shap.data}
4 \alias{xgb.shap.data}
5 \title{Prepare data for SHAP plots. To be used in xgb.plot.shap, xgb.plot.shap.summary, etc.
8 xgb.shap.data(
27 \item{features}{a vector of either column indices or of feature names to plot. When it is NULL,
28 feature importance is calculated, and \code{top_n} high ranked features are taken.}
32 \item{model}{an \code{xgb.Booster} model. It has to be provided when either \code{shap_contrib}
35 \item{trees}{passed to \code{\link{xgb.importance}} when \code{features = NULL}.}
41 \item{approxcontrib}{passed to \code{\link{predict.xgb.Booster}} when \code{shap_contrib = NULL}.}
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| H A D | xgb.plot.shap.Rd | 2 % Please edit documentation in R/xgb.plot.shap.R
3 \name{xgb.plot.shap}
4 \alias{xgb.plot.shap}
7 xgb.plot.shap(
42 feature importance is calculated, and \code{top_n} high ranked features are taken.}
49 \item{trees}{passed to \code{\link{xgb.importance}} when \code{features = NULL}.}
131 xgb.plot.shap(agaricus.test$data, model = bst, features = "odor=none")
133 xgb.plot.shap(agaricus.test$data, contr, model = bst, top_n = 12, n_col = 3)
147 xgb.plot.shap(x, model = mbst, trees = trees0, target_class = 0, top_n = 4,
149 xgb.plot.shap(x, model = mbst, trees = trees0 + 1, target_class = 1, top_n = 4,
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| H A D | xgb.plot.tree.Rd | 2 % Please edit documentation in R/xgb.plot.tree.R
3 \name{xgb.plot.tree}
4 \alias{xgb.plot.tree}
7 xgb.plot.tree(
21 \item{model}{produced by the \code{xgb.train} function.}
49 Read a tree model text dump and plot the model.
61 (corresponds to the importance of the node in the model).
77 # plot all the trees
78 xgb.plot.tree(model = bst)
80 xgb.plot.tree(model = bst, trees = 0, show_node_id = TRUE)
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| /dports/misc/xgboost/xgboost-1.5.1/R-package/man/ |
| H A D | xgb.plot.importance.Rd | 2 % Please edit documentation in R/xgb.ggplot.R, R/xgb.plot.importance.R
3 \name{xgb.ggplot.importance}
4 \alias{xgb.ggplot.importance}
5 \alias{xgb.plot.importance}
8 xgb.ggplot.importance(
17 xgb.plot.importance(
24 plot = TRUE,
33 \item{measure}{the name of importance measure to plot.
53 The \code{xgb.plot.importance} function creates a \code{barplot} (when \code{plot=TRUE})
61 \code{xgb.plot.importance} uses base R graphics, while \code{xgb.ggplot.importance} uses the ggplot…
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| H A D | xgb.plot.shap.summary.Rd | 2 % Please edit documentation in R/xgb.ggplot.R, R/xgb.plot.shap.R
3 \name{xgb.ggplot.shap.summary}
4 \alias{xgb.ggplot.shap.summary}
5 \alias{xgb.plot.shap.summary}
6 \title{SHAP contribution dependency summary plot}
8 xgb.ggplot.shap.summary(
20 xgb.plot.shap.summary(
39 feature importance is calculated, and \code{top_n} high ranked features are taken.}
46 \item{trees}{passed to \code{\link{xgb.importance}} when \code{features = NULL}.}
73 # See \code{\link{xgb.plot.shap}}.
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| H A D | xgb.shap.data.Rd | 2 % Please edit documentation in R/xgb.plot.shap.R
3 \name{xgb.shap.data}
4 \alias{xgb.shap.data}
5 \title{Prepare data for SHAP plots. To be used in xgb.plot.shap, xgb.plot.shap.summary, etc.
8 xgb.shap.data(
27 \item{features}{a vector of either column indices or of feature names to plot. When it is NULL,
28 feature importance is calculated, and \code{top_n} high ranked features are taken.}
32 \item{model}{an \code{xgb.Booster} model. It has to be provided when either \code{shap_contrib}
35 \item{trees}{passed to \code{\link{xgb.importance}} when \code{features = NULL}.}
41 \item{approxcontrib}{passed to \code{\link{predict.xgb.Booster}} when \code{shap_contrib = NULL}.}
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| H A D | xgb.plot.shap.Rd | 2 % Please edit documentation in R/xgb.plot.shap.R
3 \name{xgb.plot.shap}
4 \alias{xgb.plot.shap}
7 xgb.plot.shap(
42 feature importance is calculated, and \code{top_n} high ranked features are taken.}
49 \item{trees}{passed to \code{\link{xgb.importance}} when \code{features = NULL}.}
131 xgb.plot.shap(agaricus.test$data, model = bst, features = "odor=none")
133 xgb.plot.shap(agaricus.test$data, contr, model = bst, top_n = 12, n_col = 3)
147 xgb.plot.shap(x, model = mbst, trees = trees0, target_class = 0, top_n = 4,
149 xgb.plot.shap(x, model = mbst, trees = trees0 + 1, target_class = 1, top_n = 4,
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| H A D | xgb.plot.tree.Rd | 2 % Please edit documentation in R/xgb.plot.tree.R
3 \name{xgb.plot.tree}
4 \alias{xgb.plot.tree}
7 xgb.plot.tree(
21 \item{model}{produced by the \code{xgb.train} function.}
49 Read a tree model text dump and plot the model.
61 (corresponds to the importance of the node in the model).
77 # plot all the trees
78 xgb.plot.tree(model = bst)
80 xgb.plot.tree(model = bst, trees = 0, show_node_id = TRUE)
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| /dports/misc/py-xgboost/xgboost-1.5.1/R-package/ |
| H A D | NAMESPACE | 32 export(xgb.attr)
36 export(xgb.cv)
37 export(xgb.dump)
41 export(xgb.ggplot.importance)
43 export(xgb.importance)
47 export(xgb.plot.deepness)
48 export(xgb.plot.importance)
49 export(xgb.plot.multi.trees)
50 export(xgb.plot.shap)
51 export(xgb.plot.shap.summary)
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| /dports/misc/xgboost/xgboost-1.5.1/R-package/ |
| H A D | NAMESPACE | 32 export(xgb.attr)
36 export(xgb.cv)
37 export(xgb.dump)
41 export(xgb.ggplot.importance)
43 export(xgb.importance)
47 export(xgb.plot.deepness)
48 export(xgb.plot.importance)
49 export(xgb.plot.multi.trees)
50 export(xgb.plot.shap)
51 export(xgb.plot.shap.summary)
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| /dports/misc/py-xgboost/xgboost-1.5.1/doc/python/ |
| H A D | python_intro.rst | 19 import xgboost as xgb
55 dtrain = xgb.DMatrix(csr)
69 dtrain = xgb.DMatrix('train.svm.txt')
92 dtrain = xgb.DMatrix('train.svm.txt')
199 dtest = xgb.DMatrix(data)
211 You can use plotting module to plot importance and output tree.
213 To plot importance, use :py:meth:`xgboost.plot_importance`. This function requires ``matplotlib`` t…
217 xgb.plot_importance(bst)
219 To plot the output tree via ``matplotlib``, use :py:meth:`xgboost.plot_tree`, specifying the ordina…
223 xgb.plot_tree(bst, num_trees=2)
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| /dports/misc/xgboost/xgboost-1.5.1/doc/python/ |
| H A D | python_intro.rst | 19 import xgboost as xgb
55 dtrain = xgb.DMatrix(csr)
69 dtrain = xgb.DMatrix('train.svm.txt')
92 dtrain = xgb.DMatrix('train.svm.txt')
199 dtest = xgb.DMatrix(data)
211 You can use plotting module to plot importance and output tree.
213 To plot importance, use :py:meth:`xgboost.plot_importance`. This function requires ``matplotlib`` t…
217 xgb.plot_importance(bst)
219 To plot the output tree via ``matplotlib``, use :py:meth:`xgboost.plot_tree`, specifying the ordina…
223 xgb.plot_tree(bst, num_trees=2)
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| /dports/misc/py-xgboost/xgboost-1.5.1/demo/kaggle-otto/ |
| H A D | understandingXGBoostModel.Rmd | 127 bst.cv = xgb.cv(param=param, data = trainMatrix, label = y,
142 Feature importance
166 model <- xgb.dump(bst, with.stats = TRUE)
175 Hopefully, **XGBoost** offers a better representation: **feature importance**.
179 Then we can use the function `xgb.plot.importance`.
185 # Compute feature importance matrix
186 importance_matrix <- xgb.importance(names, model = bst)
189 xgb.plot.importance(importance_matrix[1:10,])
197 In the feature importance above, we can see the first 10 most important features.
208 Feature importance gives you feature weight information but not interaction between features.
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| /dports/misc/xgboost/xgboost-1.5.1/demo/kaggle-otto/ |
| H A D | understandingXGBoostModel.Rmd | 127 bst.cv = xgb.cv(param=param, data = trainMatrix, label = y,
142 Feature importance
166 model <- xgb.dump(bst, with.stats = TRUE)
175 Hopefully, **XGBoost** offers a better representation: **feature importance**.
179 Then we can use the function `xgb.plot.importance`.
185 # Compute feature importance matrix
186 importance_matrix <- xgb.importance(names, model = bst)
189 xgb.plot.importance(importance_matrix[1:10,])
197 In the feature importance above, we can see the first 10 most important features.
208 Feature importance gives you feature weight information but not interaction between features.
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| /dports/misc/py-xgboost/xgboost-1.5.1/R-package/vignettes/ |
| H A D | xgboostPresentation.Rmd | 170 ##### xgb.DMatrix
320 ### Manipulating xgb.DMatrix
349 ### View feature importance/influence from the learnt model
352 Feature importance is similar to R gbm package's relative influence (rel.inf).
355 importance_matrix <- xgb.importance(model = bst)
357 xgb.plot.importance(importance_matrix = importance_matrix)
369 You can plot the trees from your model using ```xgb.plot.tree``
372 xgb.plot.tree(model = bst)
386 xgb.save(bst, "xgboost.model")
413 rawVec <- xgb.serialize(bst)
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| H A D | discoverYourData.Rmd | 185 Feature importance
188 ## Measure feature importance
191 ### Build the feature importance data.table
196 importance <- xgb.importance(feature_names = colnames(sparse_matrix), model = bst)
197 head(importance)
210 #### Improvement in the interpretability of feature importance data.table
219 importanceRaw <- xgb.importance(feature_names = colnames(sparse_matrix), model = bst, data = sparse…
239 ### Plotting the feature importance
242 All these things are nice, but it would be even better to plot the results.
245 xgb.plot.importance(importance_matrix = importance)
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| /dports/misc/py-xgboost/xgboost-1.5.1/doc/R-package/ |
| H A D | xgboostPresentation.md | 211 ##### xgb.DMatrix
422 ### Manipulating xgb.DMatrix
474 ### View feature importance/influence from the learnt model
477 Feature importance is similar to R gbm package's relative influence (rel.inf).
480 importance_matrix <- xgb.importance(model = bst)
482 xgb.plot.importance(importance_matrix = importance_matrix)
512 You can plot the trees from your model using ```xgb.plot.tree``
515 xgb.plot.tree(model = bst)
530 xgb.save(bst, "xgboost.model")
564 rawVec <- xgb.save.raw(bst)
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| H A D | discoverYourData.md | 249 Feature importance
252 ## Measure feature importance
255 ### Build the feature importance data.table argument
261 importance <- xgb.importance(feature_names = sparse_matrix@Dimnames[[2]], model = bst)
262 head(importance)
295 importanceRaw <- xgb.importance(feature_names = sparse_matrix@Dimnames[[2]], model = bst, data = sp…
325 ### Plotting the feature importance argument
328 All these things are nice, but it would be even better to plot the results.
332 xgb.plot.importance(importance_matrix = importanceRaw)
336 ## Error in xgb.plot.importance(importance_matrix = importanceRaw): Importance matrix is not correc…
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| /dports/misc/xgboost/xgboost-1.5.1/R-package/vignettes/ |
| H A D | xgboostPresentation.Rmd | 170 ##### xgb.DMatrix
320 ### Manipulating xgb.DMatrix
349 ### View feature importance/influence from the learnt model
352 Feature importance is similar to R gbm package's relative influence (rel.inf).
355 importance_matrix <- xgb.importance(model = bst)
357 xgb.plot.importance(importance_matrix = importance_matrix)
369 You can plot the trees from your model using ```xgb.plot.tree``
372 xgb.plot.tree(model = bst)
386 xgb.save(bst, "xgboost.model")
413 rawVec <- xgb.serialize(bst)
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| H A D | discoverYourData.Rmd | 185 Feature importance
188 ## Measure feature importance
191 ### Build the feature importance data.table
196 importance <- xgb.importance(feature_names = colnames(sparse_matrix), model = bst)
197 head(importance)
210 #### Improvement in the interpretability of feature importance data.table
219 importanceRaw <- xgb.importance(feature_names = colnames(sparse_matrix), model = bst, data = sparse…
239 ### Plotting the feature importance
242 All these things are nice, but it would be even better to plot the results.
245 xgb.plot.importance(importance_matrix = importance)
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| /dports/misc/xgboost/xgboost-1.5.1/doc/R-package/ |
| H A D | xgboostPresentation.md | 211 ##### xgb.DMatrix
422 ### Manipulating xgb.DMatrix
474 ### View feature importance/influence from the learnt model
477 Feature importance is similar to R gbm package's relative influence (rel.inf).
480 importance_matrix <- xgb.importance(model = bst)
482 xgb.plot.importance(importance_matrix = importance_matrix)
512 You can plot the trees from your model using ```xgb.plot.tree``
515 xgb.plot.tree(model = bst)
530 xgb.save(bst, "xgboost.model")
564 rawVec <- xgb.save.raw(bst)
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| H A D | discoverYourData.md | 249 Feature importance
252 ## Measure feature importance
255 ### Build the feature importance data.table argument
261 importance <- xgb.importance(feature_names = sparse_matrix@Dimnames[[2]], model = bst)
262 head(importance)
295 importanceRaw <- xgb.importance(feature_names = sparse_matrix@Dimnames[[2]], model = bst, data = sp…
325 ### Plotting the feature importance argument
328 All these things are nice, but it would be even better to plot the results.
332 xgb.plot.importance(importance_matrix = importanceRaw)
336 ## Error in xgb.plot.importance(importance_matrix = importanceRaw): Importance matrix is not correc…
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| /dports/misc/py-xgboost/xgboost-1.5.1/ |
| H A D | NEWS.md | 125 * Fix R documentation for xgb.train. (#6764)
320 * [R] Add SHAP summary plot using ggplot2 (#5882)
357 * [R] allow `xgb.plot.importance()` calls to fill a grid (#6294)
553 * Support passing fmap to importance plot (#5719). Now importance plot can show actual names of fea…
1032 * [R] Use built-in label when xgb.DMatrix is given to xgb.cv() (#4631)
1422 - Support multiple feature importance features (#3801)
1627 - Enlarge variable importance plot to make it more visible (#3820)
1822 - `silent` in `xgb.DMatrix()`
1823 - `use_int_id` in `xgb.model.dt.tree()`
1833 - Improved `xgb.plot.tree()`
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