我想理解来自H2o R-package的h2o.predict()函数的值(结果)的含义。我意识到在某些情况下,当predict
列是1
时,p1
列的值低于p0
列。我对p0
和p1
列的解释是指每个事件的概率,所以我预计当predict=1
p1
的概率应该高于相反事件的概率(p0
)时,但它并不总是如我所示以下示例:使用prostate dataset。
这是可执行的例子:
library(h2o)
h2o.init(max_mem_size = "12g", nthreads = -1)
prostate.hex <- h2o.importFile("https://h2o-public-test-data.s3.amazonaws.com/smalldata/prostate/prostate.csv")
prostate.hex$CAPSULE <- as.factor(prostate.hex$CAPSULE)
prostate.hex$RACE <- as.factor(prostate.hex$RACE)
prostate.hex$DCAPS <- as.factor(prostate.hex$DCAPS)
prostate.hex$DPROS <- as.factor(prostate.hex$DPROS)
prostate.hex.split = h2o.splitFrame(data = prostate.hex,
ratios = c(0.70, 0.20, 0.10), seed = 1234)
train.hex <- prostate.hex.split[[1]]
validate.hex <- prostate.hex.split[[2]]
test.hex <- prostate.hex.split[[3]]
fit <- h2o.glm(y = "CAPSULE", x = c("AGE", "RACE", "PSA", "DCAPS"),
training_frame = train.hex,
validation_frame = validate.hex,
family = "binomial", nfolds = 0, alpha = 0.5)
prostate.predict = h2o.predict(object = fit, newdata = test.hex)
result <- as.data.frame(prostate.predict)
subset(result, predict == 1 & p1 < 0.4)
我得到subset
函数结果的以下输出:
predict p0 p1
11 1 0.6355974 0.3644026
17 1 0.6153021 0.3846979
23 1 0.6289063 0.3710937
25 1 0.6007919 0.3992081
31 1 0.6239587 0.3760413
对于来自test.hex
数据集的所有上述观察,预测是1
,但是p0 > p1
。
predict=1
但p1 < p0
的总观察结果为:
> nrow(subset(result, predict == 1 & p1 < p0))
[1] 14
相反,没有predict=0
p0 < p1
> nrow(subset(result, predict == 0 & p0 < p1))
[1] 0
以下是table
的predict
信息表:
> table(result$predict)
0 1
18 23
我们使用以下值作为决策变量CAPSULE
:
> levels(as.data.frame(prostate.hex)$CAPSULE)
[1] "0" "1"
有什么建议吗?
注意:具有类似主题的问题:How to interpret results of h2o.predict未解决此特定问题。
似乎(也见here)将F1 score
数据集上最大化validation
的阈值用作h2o.glm()
分类的默认阈值。我们可以观察到以下内容:
F1 score
的阈值是0.363477
。p1
概率小于该阈值的所有数据点被分类为0
类(预测为0
类的数据点具有最高的p1
概率= 0.3602365
<0.363477
)。p1
概率大于该阈值的所有数据点被分类为1
类(预测为1
类的数据点具有最低的p1
概率= 0.3644026
> 0.363477
)。
min(result[result$predict==1,]$p1)
# [1] 0.3644026
max(result[result$predict==0,]$p1)
# [1] 0.3602365
# Thresholds found by maximizing the metrics on the training dataset
fit@model$training_metrics@metrics$max_criteria_and_metric_scores
#Maximum Metrics: Maximum metrics at their respective thresholds
# metric threshold value idx
#1 max f1 0.314699 0.641975 200
#2 max f2 0.215203 0.795148 262
#3 max f0point5 0.451965 0.669856 74
#4 max accuracy 0.451965 0.707581 74
#5 max precision 0.998285 1.000000 0
#6 max recall 0.215203 1.000000 262
#7 max specificity 0.998285 1.000000 0
#8 max absolute_mcc 0.451965 0.395147 74
#9 max min_per_class_accuracy 0.360174 0.652542 127
#10 max mean_per_class_accuracy 0.391279 0.683269 97
# Thresholds found by maximizing the metrics on the validation dataset
fit@model$validation_metrics@metrics$max_criteria_and_metric_scores
#Maximum Metrics: Maximum metrics at their respective thresholds
# metric threshold value idx
#1 max f1 0.363477 0.607143 33
#2 max f2 0.292342 0.785714 51
#3 max f0point5 0.643382 0.725806 9
#4 max accuracy 0.643382 0.774194 9
#5 max precision 0.985308 1.000000 0
#6 max recall 0.292342 1.000000 51
#7 max specificity 0.985308 1.000000 0
#8 max absolute_mcc 0.643382 0.499659 9
#9 max min_per_class_accuracy 0.379602 0.650000 28
#10 max mean_per_class_accuracy 0.618286 0.702273 11
result[order(result$predict),]
# predict p0 p1
#5 0 0.703274569 0.2967254
#6 0 0.639763460 0.3602365
#13 0 0.689557497 0.3104425
#14 0 0.656764541 0.3432355
#15 0 0.696248328 0.3037517
#16 0 0.707069611 0.2929304
#18 0 0.692137408 0.3078626
#19 0 0.701482762 0.2985172
#20 0 0.705973644 0.2940264
#21 0 0.701156961 0.2988430
#22 0 0.671778898 0.3282211
#24 0 0.646735016 0.3532650
#26 0 0.646582708 0.3534173
#27 0 0.690402957 0.3095970
#32 0 0.649945017 0.3500550
#37 0 0.804937468 0.1950625
#40 0 0.717706731 0.2822933
#41 0 0.642094040 0.3579060
#1 1 0.364577068 0.6354229
#2 1 0.503432724 0.4965673
#3 1 0.406771233 0.5932288
#4 1 0.551801718 0.4481983
#7 1 0.339600779 0.6603992
#8 1 0.002978593 0.9970214
#9 1 0.378034417 0.6219656
#10 1 0.596298925 0.4037011
#11 1 0.635597359 0.3644026
#12 1 0.552662241 0.4473378
#17 1 0.615302107 0.3846979
#23 1 0.628906297 0.3710937
#25 1 0.600791894 0.3992081
#28 1 0.216571552 0.7834284
#29 1 0.559174924 0.4408251
#30 1 0.489514642 0.5104854
#31 1 0.623958696 0.3760413
#33 1 0.504691497 0.4953085
#34 1 0.582509462 0.4174905
#35 1 0.504136056 0.4958639
#36 1 0.463076505 0.5369235
#38 1 0.510908093 0.4890919
#39 1 0.469376828 0.5306232
你所描述的是0.5的阈值。事实上,将使用不同的阈值,一个最大化某个度量的阈值。默认度量标准为F1(*);如果您打印模型信息,您可以找到用于每个指标的阈值。
请参阅问题:How to understand the metrics of H2OModelMetrics Object through h2o.performance?了解更多信息(您的问题不同,这就是为什么我没有将其标记为重复)。
据我所知,你无法将F1默认值更改为h2o.predict()
或h2o.performance()
。但相反,你可以使用h2o.confusionMatrix()
鉴于你的模型fit
,并使用最大F2代替:
h2o.confusionMatrix(fit, metrics = "f2")
您也可以直接使用h2o.predict()
“p0”列,使用您自己的阈值,而不是“预测”列。 (这就是我以前做过的事。)
*:定义如下:https://github.com/h2oai/h2o-3/blob/fdde85e41bad5f31b6b841b300ce23cfb2d8c0b0/h2o-core/src/main/java/hex/AUC2.java#L34该文件的下方还显示了如何计算每个指标。