这些函数可用于将类概率估计转换为具有可选歧义区域的 class_pred 对象。
用法
make_class_pred(..., levels, ordered = FALSE, min_prob = 1/length(levels))
make_two_class_pred(
estimate,
levels,
threshold = 0.5,
ordered = FALSE,
buffer = NULL
)参数
- ...
-
对应于类别概率的数值向量。每个级别都应该有一个
levels， 和假设向量的顺序与levels相同. - levels
-
类级别的特征向量。长度应与通过
...进行的选择数量相同，或者make_two_class_pred()的长度为2。 - ordered
-
用于确定级别是否应被视为有序的单个逻辑(按给定的顺序)。这会产生一个标记为有序的
class_pred对象。 - min_prob
-
单个数值。如果任何概率小于该值(按行)，则该行被标记为不明确。
- estimate
-
对应于
levels中第一级别的类概率的单个数值向量。 - threshold
-
用于调用要标记为
levels第一个值的行的阈值的单个数值。 - buffer
-
threshold周围缓冲区的长度为 1 或 2 的数值向量，定义了歧义区域(即threshold - buffer[1]到threshold + buffer[2])。长度为 1 的向量被回收为长度 2。默认值NULL被解释为无歧义区域。
值
类 class_pred 的向量。
例子
library(dplyr)
good <- segment_logistic$.pred_good
lvls <- levels(segment_logistic$Class)
# Equivocal zone of .5 +/- .15
make_two_class_pred(good, lvls, buffer = 0.15)
#> [1] poor poor good good poor [EQ] poor good poor poor good poor poor
#> [14] good poor good poor poor [EQ] poor poor good poor good poor poor
#> [27] good [EQ] good good poor poor [EQ] good good poor poor poor poor
#> [40] poor [EQ] [EQ] poor poor poor poor poor good good poor [EQ] poor
#> [53] poor [EQ] poor [EQ] poor poor [EQ] poor poor good good poor poor
#> [66] [EQ] good poor [EQ] poor good poor [EQ] poor good poor good [EQ]
#> [79] poor [EQ] good poor poor poor good good poor good poor poor poor
#> [92] poor [EQ] good poor [EQ] good [EQ] [EQ] poor good [EQ] poor poor
#> [105] good good good [EQ] good poor poor poor good poor [EQ] poor [EQ]
#> [118] poor good good poor good poor [EQ] poor poor good good poor poor
#> [131] [EQ] [EQ] poor good good poor [EQ] poor [EQ] [EQ] poor [EQ] [EQ]
#> [144] poor [EQ] poor good [EQ] poor poor poor good good poor poor [EQ]
#> [157] good poor poor [EQ] good poor good good poor poor poor [EQ] [EQ]
#> [170] poor good good poor poor [EQ] good poor poor poor poor [EQ] good
#> [183] poor poor poor [EQ] poor good good poor good [EQ] poor good poor
#> [196] poor good poor poor poor good good [EQ] poor poor poor poor poor
#> [209] [EQ] poor good good poor [EQ] poor poor poor good poor good [EQ]
#> [222] poor good good good poor [EQ] poor poor good poor poor poor good
#> [235] good good poor poor poor [EQ] [EQ] poor poor poor [EQ] poor [EQ]
#> [248] good poor poor poor good poor poor good [EQ] good good poor [EQ]
#> [261] poor good good [EQ] [EQ] good poor poor poor poor [EQ] poor poor
#> [274] poor poor [EQ] good poor [EQ] [EQ] poor poor poor good [EQ] poor
#> [287] [EQ] good poor [EQ] poor good [EQ] good poor poor poor good poor
#> [300] good [EQ] poor poor poor good poor poor poor [EQ] good poor poor
#> [313] good poor good poor [EQ] poor poor [EQ] [EQ] poor poor poor poor
#> [326] good [EQ] poor poor [EQ] poor poor poor poor poor [EQ] good [EQ]
#> [339] poor good poor good [EQ] good poor [EQ] poor poor poor poor poor
#> [352] good good [EQ] [EQ] poor good poor good poor poor poor poor good
#> [365] poor poor poor poor poor poor poor [EQ] poor poor poor poor poor
#> [378] poor poor good poor poor poor poor poor poor [EQ] good poor poor
#> [391] poor [EQ] [EQ] good poor poor poor poor poor poor good [EQ] [EQ]
#> [404] poor poor poor poor poor poor poor good good poor poor poor poor
#> [417] poor [EQ] poor poor poor good [EQ] good good poor poor poor good
#> [430] good good good poor good poor poor poor poor poor poor good [EQ]
#> [443] [EQ] poor good good [EQ] [EQ] poor poor good poor poor good poor
#> [456] good poor poor poor good poor poor poor poor good poor poor good
#> [469] poor good good good poor good poor good good good poor poor good
#> [482] poor poor poor poor poor poor good [EQ] poor [EQ] poor poor poor
#> [495] good poor [EQ] poor [EQ] poor poor poor poor poor poor good good
#> [508] poor [EQ] [EQ] [EQ] poor poor poor poor good [EQ] good poor poor
#> [521] good good poor [EQ] poor poor [EQ] poor good poor poor good poor
#> [534] poor poor poor poor good [EQ] poor good good poor poor good poor
#> [547] good good poor poor good poor good poor [EQ] poor poor poor poor
#> [560] [EQ] good poor good good poor poor poor good good poor poor good
#> [573] [EQ] [EQ] poor [EQ] poor poor poor [EQ] poor good poor good good
#> [586] poor poor poor poor good [EQ] good poor good [EQ] [EQ] poor poor
#> [599] [EQ] [EQ] poor good good good [EQ] good poor poor poor [EQ] poor
#> [612] good poor good [EQ] poor poor poor good good poor good poor poor
#> [625] poor poor poor good poor [EQ] good [EQ] good poor good poor good
#> [638] poor poor [EQ] [EQ] poor poor poor poor poor good good poor poor
#> [651] poor poor poor poor good good poor good poor good poor good poor
#> [664] poor poor [EQ] poor poor good poor poor good good good poor poor
#> [677] poor [EQ] poor good good [EQ] good poor good poor poor poor [EQ]
#> [690] poor poor [EQ] [EQ] good [EQ] poor good poor poor good good poor
#> [703] [EQ] poor good poor poor [EQ] [EQ] [EQ] poor good poor good good
#> [716] good good poor poor poor good poor good poor poor [EQ] poor poor
#> [729] poor poor poor poor [EQ] good good good poor [EQ] poor poor poor
#> [742] good poor good good [EQ] poor good poor [EQ] poor poor poor [EQ]
#> [755] good good poor poor poor good poor good poor good [EQ] poor good
#> [768] [EQ] poor [EQ] good poor good [EQ] poor good poor poor good poor
#> [781] poor good good good poor poor poor poor poor good poor [EQ] poor
#> [794] poor poor good [EQ] poor good [EQ] [EQ] good poor good poor poor
#> [807] poor poor poor poor poor [EQ] poor good poor poor poor poor good
#> [820] poor good good poor poor poor poor poor good [EQ] poor good poor
#> [833] poor poor poor poor poor poor [EQ] poor poor poor poor poor good
#> [846] good good poor poor poor poor poor poor poor poor good [EQ] [EQ]
#> [859] [EQ] poor good [EQ] poor poor poor [EQ] good poor good good poor
#> [872] good poor poor good [EQ] [EQ] [EQ] poor poor poor poor [EQ] good
#> [885] poor good poor good poor poor poor poor good poor poor poor poor
#> [898] poor poor poor poor [EQ] poor poor [EQ] good [EQ] good poor poor
#> [911] poor good [EQ] poor good poor poor poor poor good poor poor good
#> [924] good poor poor good poor [EQ] poor good poor good good good poor
#> [937] poor good good poor poor [EQ] [EQ] poor good poor poor good [EQ]
#> [950] [EQ] poor [EQ] poor good [EQ] [EQ] poor good poor poor poor good
#> [963] poor poor poor poor good poor poor [EQ] poor poor poor good good
#> [976] poor [EQ] poor poor poor good poor poor good poor [EQ] good good
#> [989] good good poor [EQ] poor good poor poor poor poor good good good
#> [1002] good good poor good [EQ] good poor poor good
#> Levels: good poor
#> Reportable: 83.2%
# Equivocal zone of c(.5 - .05, .5 + .15)
make_two_class_pred(good, lvls, buffer = c(0.05, 0.15))
#> [1] poor poor good good poor poor poor good poor poor good poor poor
#> [14] good poor good poor poor [EQ] poor poor good poor good poor poor
#> [27] good poor good good poor poor [EQ] good good poor poor poor poor
#> [40] poor [EQ] poor poor poor poor poor poor good good poor [EQ] poor
#> [53] poor poor poor poor poor poor [EQ] poor poor good good poor poor
#> [66] poor good poor [EQ] poor good poor [EQ] poor good poor good poor
#> [79] poor [EQ] good poor poor poor good good poor good poor poor poor
#> [92] poor [EQ] good poor [EQ] good [EQ] poor poor good [EQ] poor poor
#> [105] good good good poor good poor poor poor good poor poor poor [EQ]
#> [118] poor good good poor good poor [EQ] poor poor good good poor poor
#> [131] [EQ] poor poor good good poor [EQ] poor [EQ] [EQ] poor poor [EQ]
#> [144] poor [EQ] poor good poor poor poor poor good good poor poor poor
#> [157] good poor poor [EQ] good poor good good poor poor poor [EQ] poor
#> [170] poor good good poor poor [EQ] good poor poor poor poor [EQ] good
#> [183] poor poor poor [EQ] poor good good poor good poor poor good poor
#> [196] poor good poor poor poor good good poor poor poor poor poor poor
#> [209] poor poor good good poor [EQ] poor poor poor good poor good [EQ]
#> [222] poor good good good poor [EQ] poor poor good poor poor poor good
#> [235] good good poor poor poor [EQ] poor poor poor poor [EQ] poor poor
#> [248] good poor poor poor good poor poor good [EQ] good good poor [EQ]
#> [261] poor good good [EQ] poor good poor poor poor poor [EQ] poor poor
#> [274] poor poor [EQ] good poor poor [EQ] poor poor poor good [EQ] poor
#> [287] [EQ] good poor poor poor good [EQ] good poor poor poor good poor
#> [300] good [EQ] poor poor poor good poor poor poor [EQ] good poor poor
#> [313] good poor good poor poor poor poor [EQ] [EQ] poor poor poor poor
#> [326] good poor poor poor [EQ] poor poor poor poor poor [EQ] good [EQ]
#> [339] poor good poor good poor good poor [EQ] poor poor poor poor poor
#> [352] good good poor [EQ] poor good poor good poor poor poor poor good
#> [365] poor poor poor poor poor poor poor poor poor poor poor poor poor
#> [378] poor poor good poor poor poor poor poor poor [EQ] good poor poor
#> [391] poor [EQ] [EQ] good poor poor poor poor poor poor good [EQ] [EQ]
#> [404] poor poor poor poor poor poor poor good good poor poor poor poor
#> [417] poor [EQ] poor poor poor good [EQ] good good poor poor poor good
#> [430] good good good poor good poor poor poor poor poor poor good [EQ]
#> [443] poor poor good good [EQ] [EQ] poor poor good poor poor good poor
#> [456] good poor poor poor good poor poor poor poor good poor poor good
#> [469] poor good good good poor good poor good good good poor poor good
#> [482] poor poor poor poor poor poor good poor poor [EQ] poor poor poor
#> [495] good poor poor poor [EQ] poor poor poor poor poor poor good good
#> [508] poor [EQ] [EQ] [EQ] poor poor poor poor good poor good poor poor
#> [521] good good poor poor poor poor [EQ] poor good poor poor good poor
#> [534] poor poor poor poor good poor poor good good poor poor good poor
#> [547] good good poor poor good poor good poor poor poor poor poor poor
#> [560] poor good poor good good poor poor poor good good poor poor good
#> [573] [EQ] poor poor poor poor poor poor poor poor good poor good good
#> [586] poor poor poor poor good [EQ] good poor good poor [EQ] poor poor
#> [599] poor [EQ] poor good good good [EQ] good poor poor poor poor poor
#> [612] good poor good poor poor poor poor good good poor good poor poor
#> [625] poor poor poor good poor poor good poor good poor good poor good
#> [638] poor poor [EQ] [EQ] poor poor poor poor poor good good poor poor
#> [651] poor poor poor poor good good poor good poor good poor good poor
#> [664] poor poor [EQ] poor poor good poor poor good good good poor poor
#> [677] poor [EQ] poor good good [EQ] good poor good poor poor poor poor
#> [690] poor poor poor [EQ] good [EQ] poor good poor poor good good poor
#> [703] poor poor good poor poor [EQ] [EQ] poor poor good poor good good
#> [716] good good poor poor poor good poor good poor poor [EQ] poor poor
#> [729] poor poor poor poor [EQ] good good good poor poor poor poor poor
#> [742] good poor good good poor poor good poor [EQ] poor poor poor [EQ]
#> [755] good good poor poor poor good poor good poor good [EQ] poor good
#> [768] [EQ] poor [EQ] good poor good [EQ] poor good poor poor good poor
#> [781] poor good good good poor poor poor poor poor good poor [EQ] poor
#> [794] poor poor good [EQ] poor good poor poor good poor good poor poor
#> [807] poor poor poor poor poor poor poor good poor poor poor poor good
#> [820] poor good good poor poor poor poor poor good [EQ] poor good poor
#> [833] poor poor poor poor poor poor poor poor poor poor poor poor good
#> [846] good good poor poor poor poor poor poor poor poor good [EQ] poor
#> [859] [EQ] poor good poor poor poor poor [EQ] good poor good good poor
#> [872] good poor poor good [EQ] poor [EQ] poor poor poor poor [EQ] good
#> [885] poor good poor good poor poor poor poor good poor poor poor poor
#> [898] poor poor poor poor poor poor poor poor good poor good poor poor
#> [911] poor good [EQ] poor good poor poor poor poor good poor poor good
#> [924] good poor poor good poor [EQ] poor good poor good good good poor
#> [937] poor good good poor poor poor poor poor good poor poor good [EQ]
#> [950] poor poor [EQ] poor good [EQ] poor poor good poor poor poor good
#> [963] poor poor poor poor good poor poor [EQ] poor poor poor good good
#> [976] poor poor poor poor poor good poor poor good poor poor good good
#> [989] good good poor [EQ] poor good poor poor poor poor good good good
#> [1002] good good poor good [EQ] good poor poor good
#> Levels: good poor
#> Reportable: 89.8%
# These functions are useful alongside dplyr::mutate()
segment_logistic %>%
mutate(
.class_pred = make_two_class_pred(
estimate = .pred_good,
levels = levels(Class),
buffer = 0.15
)
)
#> # A tibble: 1,010 × 4
#> .pred_poor .pred_good Class .class_pred
#> <dbl> <dbl> <fct> <clss_prd>
#> 1 0.986 0.0142 poor poor
#> 2 0.897 0.103 poor poor
#> 3 0.118 0.882 good good
#> 4 0.102 0.898 good good
#> 5 0.991 0.00914 poor poor
#> 6 0.633 0.367 good [EQ]
#> 7 0.770 0.230 good poor
#> 8 0.00842 0.992 good good
#> 9 0.995 0.00458 poor poor
#> 10 0.765 0.235 poor poor
#> # ℹ 1,000 more rows
# Multi-class example
# Note that we provide class probability columns in the same
# order as the levels
species_probs %>%
mutate(
.class_pred = make_class_pred(
.pred_bobcat, .pred_coyote, .pred_gray_fox,
levels = levels(Species),
min_prob = .5
)
)
#> # A tibble: 110 × 5
#> Species .pred_bobcat .pred_coyote .pred_gray_fox .class_pred
#> <fct> <dbl> <dbl> <dbl> <clss_prd>
#> 1 gray_fox 0.0976 0.0530 0.849 gray_fox
#> 2 gray_fox 0.155 0.139 0.706 gray_fox
#> 3 bobcat 0.501 0.0880 0.411 bobcat
#> 4 gray_fox 0.256 0 0.744 gray_fox
#> 5 gray_fox 0.463 0.287 0.250 [EQ]
#> 6 bobcat 0.811 0 0.189 bobcat
#> 7 bobcat 0.911 0.0888 0 bobcat
#> 8 bobcat 0.898 0.0517 0.0500 bobcat
#> 9 bobcat 0.771 0.229 0 bobcat
#> 10 bobcat 0.623 0.325 0.0517 bobcat
#> # ℹ 100 more rows
相关用法
- R probably append_class_pred 添加 class_pred 列
- R probably cal_plot_logistic 通过逻辑回归绘制概率校准图
- R probably class_pred 创建类别预测对象
- R probably cal_plot_breaks 通过分箱绘制概率校准图
- R probably cal_estimate_multinomial 使用多项校准模型来计算新的概率
- R probably cal_validate_logistic 使用和不使用逻辑校准来测量性能
- R probably cal_validate_isotonic_boot 使用和不使用袋装等渗回归校准来测量性能
- R probably threshold_perf 生成跨概率阈值的性能指标
- R probably cal_estimate_beta 使用 Beta 校准模型来计算新概率
- R probably cal_plot_regression 回归校准图
- R probably as_class_pred 强制转换为 class_pred 对象
- R probably levels.class_pred 提取class_pred级别
- R probably cal_estimate_isotonic 使用等渗回归模型来校准模型预测。
- R probably locate-equivocal 找到模棱两可的值
- R probably cal_estimate_logistic 使用逻辑回归模型来校准概率
- R probably int_conformal_quantile 通过保形推理和分位数回归预测区间
- R probably cal_validate_multinomial 使用和不使用多项式校准来测量性能
- R probably cal_apply 对一组现有预测应用校准
- R probably reportable_rate 计算报告率
- R probably cal_validate_linear 使用和不使用线性回归校准来测量性能
- R probably cal_estimate_isotonic_boot 使用引导等渗回归模型来校准概率
- R probably cal_plot_windowed 通过移动窗口绘制概率校准图
- R probably int_conformal_split 通过分割共形推理预测区间
- R probably is_class_pred 测试对象是否继承自class_pred
- R probably cal_validate_isotonic 使用和不使用等渗回归校准来测量性能
注：本文由纯净天空筛选整理自Max Kuhn等大神的英文原创作品 Create a class_pred vector from class probabilities。非经特殊声明，原始代码版权归原作者所有，本译文未经允许或授权，请勿转载或复制。