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這是一般的虹膜建模代碼和結果:如何訪問分類決策樹結果和混淆矩陣結果?
This is the general iris modeling code and result:
> library(party)
> library(rpart)
> library(tree)
> library(caret)
> train = sample(1:nrow(iris),nrow(iris)* 0.7)
> Training_set = iris[train,]
> Test_set = iris[-train,]
> iris_ctree = ctree(Species~., data = Training_set)
> iris_ctree
Conditional inference tree with 4 terminal nodes
Response: Species
Inputs: Sepal.Length, Sepal.Width, Petal.Length, Petal.Width
Number of observations: 105
1) Petal.Length <= 1.9; criterion = 1, statistic = 97.056
2)* weights = 30
1) Petal.Length > 1.9
3) Petal.Width <= 1.7; criterion = 1, statistic = 48.636
4) Petal.Length <= 4.7; criterion = 0.998, statistic = 12.282
5)* weights = 36
4) Petal.Length > 4.7
6)* weights = 7
3) Petal.Width > 1.7
7)* weights = 32
> plot(iris_ctree)
> pred = predict(iris_ctree, Test_set)
> confusionMatrix(pred, Test_set$Species)
Confusion Matrix and Statistics
Reference
Prediction setosa versicolor
setosa 20 0
versicolor 0 10
virginica 0 0
Reference
Prediction virginica
setosa 0
versicolor 1
virginica 14
Overall Statistics
Accuracy :
95% CI :
No Information Rate :
P-Value [Acc > NIR] :
Kappa :
Mcnemar's Test P-Value :
0.9778
(0.8823, 0.9994)
0.4444
8.12e-15
0.9655
NA
Statistics by Class:
Class: setosa
Sensitivity 1.0000
Specificity 1.0000
Pos Pred Value 1.0000
Neg Pred Value 1.0000
Prevalence 0.4444
Detection Rate 0.4444
Detection Prevalence 0.4444
Balanced Accuracy 1.0000
Class: versicolor
Sensitivity 1.0000
Specificity 0.9714
Pos Pred Value 0.9091
Neg Pred Value 1.0000
Prevalence 0.2222
Detection Rate 0.2222
Detection Prevalence 0.2444
Balanced Accuracy 0.9857
Class: virginica
Sensitivity 0.9333
Specificity 1.0000
Pos Pred Value 1.0000
Neg Pred Value 0.9677
Prevalence 0.3333
Detection Rate 0.3111
Detection Prevalence 0.3111
Balanced Accuracy 0.9667
我想知道如何訪問「Ctree」的特定節點的值。例如,接近最低分支「7」的值。
我想知道如何處理混淆矩陣的價值。例如,要接近準確度值。
這個問題的原因是我必須用r對數據庫中的各種數據建模並得到結果。如果你能給我一個提示,我會很感激。