View source: R/xgb.plot.importance.R

xgb.ggplot.importance | R Documentation |

Represents previously calculated feature importance as a bar graph.
`xgb.plot.importance`

uses base R graphics, while `xgb.ggplot.importance`

uses the ggplot backend.

```
xgb.ggplot.importance(
importance_matrix = NULL,
top_n = NULL,
measure = NULL,
rel_to_first = FALSE,
n_clusters = c(1:10),
...
)
xgb.plot.importance(
importance_matrix = NULL,
top_n = NULL,
measure = NULL,
rel_to_first = FALSE,
left_margin = 10,
cex = NULL,
plot = TRUE,
...
)
```

`importance_matrix` |
a |

`top_n` |
maximal number of top features to include into the plot. |

`measure` |
the name of importance measure to plot.
When |

`rel_to_first` |
whether importance values should be represented as relative to the highest ranked feature. See Details. |

`n_clusters` |
(ggplot only) a |

`...` |
other parameters passed to |

`left_margin` |
(base R barplot) allows to adjust the left margin size to fit feature names.
When it is NULL, the existing |

`cex` |
(base R barplot) passed as |

`plot` |
(base R barplot) whether a barplot should be produced. If FALSE, only a data.table is returned. |

The graph represents each feature as a horizontal bar of length proportional to the importance of a feature.
Features are shown ranked in a decreasing importance order.
It works for importances from both `gblinear`

and `gbtree`

models.

When `rel_to_first = FALSE`

, the values would be plotted as they were in `importance_matrix`

.
For gbtree model, that would mean being normalized to the total of 1
("what is feature's importance contribution relative to the whole model?").
For linear models, `rel_to_first = FALSE`

would show actual values of the coefficients.
Setting `rel_to_first = TRUE`

allows to see the picture from the perspective of
"what is feature's importance contribution relative to the most important feature?"

The ggplot-backend method also performs 1-D clustering of the importance values, with bar colors corresponding to different clusters that have somewhat similar importance values.

The `xgb.plot.importance`

function creates a `barplot`

(when `plot=TRUE`

)
and silently returns a processed data.table with `n_top`

features sorted by importance.

The `xgb.ggplot.importance`

function returns a ggplot graph which could be customized afterwards.
E.g., to change the title of the graph, add `+ ggtitle("A GRAPH NAME")`

to the result.

`barplot`

.

```
data(agaricus.train)
## Keep the number of threads to 2 for examples
nthread <- 2
data.table::setDTthreads(nthread)
bst <- xgboost(
data = agaricus.train$data, label = agaricus.train$label, max_depth = 3,
eta = 1, nthread = nthread, nrounds = 2, objective = "binary:logistic"
)
importance_matrix <- xgb.importance(colnames(agaricus.train$data), model = bst)
xgb.plot.importance(importance_matrix, rel_to_first = TRUE, xlab = "Relative importance")
(gg <- xgb.ggplot.importance(importance_matrix, measure = "Frequency", rel_to_first = TRUE))
gg + ggplot2::ylab("Frequency")
```

Embedding an R snippet on your website

Add the following code to your website.

For more information on customizing the embed code, read Embedding Snippets.