How to predict one variable depending upon two other variable in Random Forest?

Question

Instead of modeling the function as an ARIMA process, I am trying to use random forests and gradient boosting as regression techniques. In the problem setup, the predictors are t_2, and t_1 and the predicted variable is t. Using the period between January 1974 and December 1978 as a training set, and using 1979 as a test set. I am trying to make a Random Forest model in R, but I am stuck at the below part, where I am getting No. of variables tried at each split: 1 and sometimes OOB is 100%. Could anyone please help me out? Thanks In advance

library(randomForest)
library(caret)
library(e1071)
library(ranger)
#converting ldeath timeseries to vector
ldeathsVector <- as.vector(ldeaths)
#dividing the vectors into 3 vectors 
t_2<- ldeathsVector[1:70]
t_1<- ldeathsVector[2:71]
t<-ldeathsVector[3:72]
ldeathDataFrame <- data.frame(t_2=t_2,t_1 =t_1,t=t)
ldeathTraining <- ldeathDataFrame[1:58,]
ldeathTesting <- ldeathDataFrame[59:70,]
ldeathTraining <- ldeathTraining[complete.cases(ldeathTraining),]
ldeathTesting <- ldeathTraining[complete.cases(ldeathTesting),]
formula <- paste(t, "~", paste(t_1, t_2 = " + "))
ldeathRandomForest <- randomForest(t ~ t_1+t_2, 
                     ldeathTraining, 
                     num.trees = 510,importance = TRUE)

Answer 1

Random forests generate trees using a random subset of predictor variables at each split, and more typically they are used where there is a large number of predictors. Part of the robustness of random forests (for datasets with many more predictors) comes from this random selection, repeated many times.

The function's default for the number of variables to use in each tree is the square root of the number of variables for classification, or the number of variables divided by three for regression. As you only have two predictors, this works out as 1. I suggest you try passing in this parameter (mtry) as 2 so that both are included each time:

ldeathRandomForest <- randomForest(t ~ t_1+t_2, 
                 ldeathTraining, 
                 num.trees = 510,importance = TRUE, mtry=2)