如何在模型输出中列出每个预测的概率

Question

使用修改后的 Iris 数据集测试一些预测物种的模型。现在仅限于 SVM 和随机森林。运行这在 R-studio 中。

简化设置：

    library(caret)

    #data
    data(iris)

    #rename
    dataset <- iris

    #smaller sample
    sample_data <- dataset[sample(nrow(dataset), 60), ]

    #create some noise so model is less-than-perfect
    noise_df <- data.frame(
          Sepal.Length = c(5.7, 5.7, 5.7, 5.7, 5.7, 5.7, 5.7, 5.7, 7.0, 7.0, 7.0, 7.0, 7.0, 7.0, 7.0, 7.0, 5.0, 5.0, 5.0, 5.0, 5.0, 5.0), 
          Sepal.Width = c(3.8, 3.8, 3.8, 3.8, 3.8, 3.8, 3.8, 3.8, 2.7, 2.7, 2.7, 2.8, 2.8, 2.8, 2.8, 2.8, 3.1, 3.1, 3.1, 3.1, 3.1, 3.1), 
          Petal.Length = c(5.2, 5.2, 5.3, 5.3, 5.4, 5.4, 5.4, 5.4, 5.5, 5.5, 5.5, 5.6, 5.6, 5.7, 5.7, 5.8, 1.3, 1.3, 1.3, 1.3, 1.3, 1.3), 
          Petal.Width = c(1.8, 1.8, 1.8, 1.9, 1.9, 1.9, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2), 
          Species = c("setosa","setosa", "setosa","setosa", "setosa","setosa","setosa","setosa", "setosa","setosa", "setosa", "setosa", "setosa", "setosa", "setosa", "setosa", "virginica", "virginica", "virginica", "virginica", "virginica", "virginica")
          )

    #combine sample with noise
    dataset2 <- rbind(sample_data, noise_df)

    #split data into train/test
    set.seed(7)
    validation_index <- createDataPartition(dataset2$Species, p=0.70, list=FALSE)
    test_set <- dataset2[-validation_index,]
    train_set <- dataset2[validation_index,]

    #====================
    #build models
    #====================

    control <- trainControl(method="cv", number=10)
    metric <- "Accuracy"

    #random forest model
    set.seed(3)
    fit.rf <- train(Species~., data=train_set, method="rf", metric=metric, trControl=control)

    #svm model
    set.seed(3)
    fit.svm <- train(Species~., data=train_set, method="svmRadial", metric=metric, trControl=control)


    #====================
    #run model on test
    #====================
    predictions <- predict(fit.svm, test_set)
    confusionMatrix(predictions, test_set$Species)

混淆矩阵输出：

                Reference
    Prediction   setosa versicolor virginica
      setosa         11          0         3
      versicolor      0          3         0
      virginica       0          1         5

我想知道是否可以列出每个预测的概率。例如：

        setosa  versicolor  virginica   predicted
    1   0.9     0.0         0.1         setosa
    2   0.1     0.8         0.1         versicolor
    3   0.33    0.33        0.33        virginica

我猜想随机森林可能只列出了 0 和 1，但想知道 SVM 是否可以选择像上面的例子那样分解概率。如果是这样，我不确定如何塑造我的数据或要使用的功能。它是 decision_function 还是 predict_proba 函数，但我不清楚如何在 r.

中正确执行它

Answer 1

对于随机森林，概率是预测每个标签的决策树的比例，你可以使用 predict(.. , type="prob") :

data.frame(predict(fit.rf,type="prob", newdata=test_set),
           predicted=predict(fit.rf, newdata=test_set))

       setosa versicolor virginica  predicted
147  0.016      0.002     0.982  virginica
15   0.908      0.068     0.024     setosa
103  0.486      0.000     0.514  virginica
118  0.416      0.056     0.528  virginica
129  0.344      0.000     0.656  virginica
39   0.388      0.080     0.532  virginica

对于 kernlab svm，您需要设置 prob.model = TRUE:

set.seed(3)
fit.svm <- train(Species~., data=train_set, method="svmRadial", metric=metric, trControl=control, prob.model = TRUE)

data.frame(predict(fit.svm,newdata=test_set,type="prob"),
           predicted=predict(fit.svm,newdata=test_set))

        setosa  versicolor  virginica  predicted
1  0.129916071 0.051873046 0.81821088  virginica
2  0.884025291 0.030853736 0.08512097     setosa
3  0.129054108 0.006256384 0.86468951  virginica
4  0.104952659 0.124066424 0.77098092  virginica

如何在模型输出中列出每个预测的概率

How to list probability for each prediction in model output

r

svm

probability

r-caret