# voteAllRules: internal In inTrees: Interpret Tree Ensembles

## Description

Predictions from a rule set

## Usage

 `1` ```voteAllRules(ruleMetric, X, type = "r", method = "median") ```

## Arguments

 `ruleMetric` rules and metrics `X` predictor variable matrix `type` regression or classification `method` for regression, use median or average

## Value

predictions from the rule set

## Examples

 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39``` ```##---- Should be DIRECTLY executable !! ---- ##-- ==> Define data, use random, ##-- or do help(data=index) for the standard data sets. ## The function is currently defined as function (ruleMetric, X, type = "r", method = "median") { xVoteList = vector("list", nrow(X)) predY <- rep("", nrow(X)) for (i in 1:nrow(ruleMetric)) { ixMatch <- eval(parse(text = paste("which(", ruleMetric[i, "condition"], ")"))) if (length(ixMatch) == 0) next for (ii in ixMatch) { xVoteList[[ii]] = c(xVoteList[[ii]], ruleMetric[i, "pred"]) } } for (i in 1:length(xVoteList)) { thisV <- xVoteList[[i]] if (length(thisV) == 0) next if (type == "c") predY[i] <- names(table(thisV)[which.max(table(thisV))]) if (type == "r") { thisV = as.numeric(thisV) if (method == "median") { predY[i] <- median(thisV) } else { predY[i] <- mean(thisV) } } } if (type == "r") predY <- as.numeric(predY) return(predY) } ```

inTrees documentation built on May 29, 2017, 2:54 p.m.