Description Usage Arguments Details Value Examples

View source: R/plot_importance.R

This functions plots selected measures of importance for variables and interactions. It is possible to visualise importance table in two ways: radar plot with six measures and scatter plot with two choosen measures.

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`x` |
a result from the |

`...` |
other parameters. |

`top` |
number of positions on the plot or NULL for all variable. Default 10. |

`radar` |
TRUE/FALSE. If TRUE the plot shows six measures of variables' or interactions' importance in the model. If FALSE the plot containing two chosen measures of variables' or interactions' importance in the model. |

`text_start_point` |
place, where the names of the particular feature start. Available for 'radar=TRUE'. Range from 0 to 1. Default 0.5. |

`text_size` |
size of the text on the plot. Default 3.5. |

`xmeasure` |
measure on the x-axis.Available for 'radar=FALSE'. Default "sumCover". |

`ymeasure` |
measure on the y-axis. Available for 'radar=FALSE'. Default "sumGain". |

Available measures:

"sumGain" - sum of Gain value in all nodes, in which given variable occurs,

"sumCover" - sum of Cover value in all nodes, in which given variable occurs; for LightGBM models: number of observation, which pass through the node,

"mean5Gain" - mean gain from 5 occurrences of given variable with the highest gain,

"meanGain" - mean Gain value in all nodes, in which given variable occurs,

"meanCover" - mean Cover value in all nodes, in which given variable occurs; for LightGBM models: mean number of observation, which pass through the node,

"freqency" - number of occurrences in the nodes for given variable.

Additionally for plots with single variables:

"meanDepth" - mean depth weighted by gain,

"numberOfRoots" - number of occurrences in the root,

"weightedRoot" - mean number of occurrences in the root, which is weighted by gain.

a ggplot object

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library("EIX")
library("Matrix")
sm <- sparse.model.matrix(left ~ . - 1, data = HR_data)
library("xgboost")
param <- list(objective = "binary:logistic", max_depth = 2)
xgb_model <- xgboost(sm, params = param, label = HR_data[, left] == 1, nrounds = 25, verbose=0)
imp <- importance(xgb_model, sm, option = "both")
imp
plot(imp, top = 10)
imp <- importance(xgb_model, sm, option = "variables")
imp
plot(imp, top = nrow(imp))
imp <- importance(xgb_model, sm, option = "interactions")
imp
plot(imp, top = nrow(imp))
imp <- importance(xgb_model, sm, option = "variables")
imp
plot(imp, top = NULL, radar = FALSE, xmeasure = "sumCover", ymeasure = "sumGain")
library(lightgbm)
train_data <- lgb.Dataset(sm, label = HR_data[, left] == 1)
params <- list(objective = "binary", max_depth = 2)
lgb_model <- lgb.train(params, train_data, 25)
imp <- importance(lgb_model, sm, option = "both")
imp
plot(imp, top = nrow(imp))
imp <- importance(lgb_model, sm, option = "variables")
imp
plot(imp, top = NULL, radar = FALSE, xmeasure = "sumCover", ymeasure = "sumGain")
``` |

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