将先前计算的特征贡献可视化为条形图。
lgb.plot.interpretation(
tree_interpretation_dt,
top_n = 10L,
cols = 1L,
left_margin = 10L,
cex = NULL
)
由 lgb.interprete
返回的 data.table
。
在图中包含的最大顶部特征数量。
布局的列号,仅用于多类别分类的特征贡献。
(base R 条形图) 允许调整左边距大小以适应特征名称。
(base R 条形图) 作为 cex.names
参数传递给 barplot
。
lgb.plot.interpretation
函数创建一个 barplot
。
该图将每个特征表示为一个水平条,其长度与特征的定义贡献成比例。特征按贡献递减的顺序列出。
# \donttest{
Logit <- function(x) {
log(x / (1.0 - x))
}
data(agaricus.train, package = "lightgbm")
labels <- agaricus.train$label
dtrain <- lgb.Dataset(
agaricus.train$data
, label = labels
)
set_field(
dataset = dtrain
, field_name = "init_score"
, data = rep(Logit(mean(labels)), length(labels))
)
data(agaricus.test, package = "lightgbm")
params <- list(
objective = "binary"
, learning_rate = 0.1
, max_depth = -1L
, min_data_in_leaf = 1L
, min_sum_hessian_in_leaf = 1.0
, num_threads = 2L
)
model <- lgb.train(
params = params
, data = dtrain
, nrounds = 5L
)
#> [LightGBM] [Info] Number of positive: 3140, number of negative: 3373
#> [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000739 seconds.
#> You can set `force_row_wise=true` to remove the overhead.
#> And if memory is not enough, you can set `force_col_wise=true`.
#> [LightGBM] [Info] Total Bins 232
#> [LightGBM] [Info] Number of data points in the train set: 6513, number of used features: 116
#> [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
#> [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
#> [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
#> [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
#> [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
tree_interpretation <- lgb.interprete(
model = model
, data = agaricus.test$data
, idxset = 1L:5L
)
lgb.plot.interpretation(
tree_interpretation_dt = tree_interpretation[[1L]]
, top_n = 3L
)
# }