How to interpret concretely the misclassification error? I'm reading about Cart classification with rpart on R, and after all we should compute the misclassification error, 
given that 
y is the column that stocks classes, 
and x is the variable columns
and fit=rpart(y~.,x) 
How Can we interpret this value W=sum(Y==predict(fit,x,type="class"))/length(Y)?
 A: The last formula may not be accurate but it seems to be the proportion of fitted values where it is classified as a certain class.
Below is an example and the response is a binary variable (H or L). What the last formula seems to aim would be length(fit.val[fit.val=="H"])/length(df$y) or length(fit.val[fit.val=="L"])/length(df$y).
Finally it is normally the confusion matrix that classification results are assessed. As shown in cm, the diagonal elements are correct classification while off-diagonal elements are error whether it is false-positive or false-negative. Therefore mean misclassification error can be obtained by (1 - correct classification proportion) - 1 - (sum(diag(cm))/sum(cm))
library(rpart)
set.seed(1237)
df <- data.frame(y = sample(c("H","L"), 100, replace = T),
                 x = rnorm(100))
fit <- rpart(y ~ x, data = df)

# fitted values
fit.val <- predict(fit, type = "class")

# proportion that classified as H or L
length(fit.val[fit.val=="H"])/length(df$y)
# [1] 0.51
length(fit.val[fit.val=="L"])/length(df$y)
# [1] 0.49

# confusion table
cm <- table(actual = df$y, fitted = fit.val)
cm

#         fitted
# actual  H  L
#      H 36 11
#      L 15 38

# mean misclassification error
mmce <- 1 - (sum(diag(cm))/sum(cm))
mmce
# [1] 0.26

