我有一个如下模型：
model
Random Forest
56 samples
100 predictors
2 classes: 'control', 't1d'
No pre-processing
Resampling: Leave-One-Out Cross-Validation
Summary of sample sizes: 55, 55, 55, 55, 55, 55, ...
Resampling results across tuning parameters:
mtry ROC Sens Spec
2 0.08673469 0.10714286 0.28571429
104 0.00000000 0.00000000 0.03571429
5499 0.00000000 0.03571429 0.00000000
ROC was used to select the optimal model using the largest value.
The final value used for the model was mtry = 2.
它表明我只有100个预测变量。但是，当我运行varImp时，这是输出。
rf variable importance
only 20 most important variables shown (out of 5500)
Overall
v720.004520377 100.00
v950.0010273908 91.59
v830.0005474310 88.41
v760.00021678889 86.73
v30.011279394 85.48
v310.002027226 84.64
v600.0030823047 82.53
v70.02515785 82.40
v110.002849240 78.08
v740.004275572 75.37
v170.00045555545 74.44
v170.00042719287 73.48
v70.01859216 72.23
v190.01569173 70.90
v700.013340430 70.70
v330.02143915 70.46
v560.013864934 69.61
v340.014476069 69.32
v810.002786661 69.18
v100.008557477 67.65
小数点后的数字是什么意思？我的功能被命名为“v1”到“v100”。为什么这说我有5500个变量？
如果您有任何想法，为什么输出看起来像这样我会很感激。
由于