Displaying 2 results from an estimated 2 matches for "y_train_".
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2023 May 09
1
RandomForest tuning the parameters
...tResample(predictions ,
> y_test)['RMSE']^2 ))
> > print(paste0('R2: ' ,caret::postResample(predictions ,
> y_test)['Rsquared'] ))
> >
> >
> > #Tuning the parameters
> > N=500 #length(X_train)
> > X_train_ = X_train[1:N , ]
> > y_train_ = y_train[1:N]
> >
> > seed <-7
> > metric<-'RMSE'
> >
> > customRF <- list(type = "Regression", library = "randomForest", loop =
> NULL)
> >
> > customRF$parameters <- data.frame(parameter = c("maxnodes"...
2023 May 08
1
RandomForest tuning the parameters
...: ' , mae(y_test,predictions) ))
print(paste0('MSE: ' ,caret::postResample(predictions , y_test)['RMSE']^2 ))
print(paste0('R2: ' ,caret::postResample(predictions , y_test)['Rsquared'] ))
?
#Tuning the parameters
N=500 #length(X_train)
X_train_ = X_train[1:N , ]
y_train_ = y_train[1:N]
seed <-7
metric<-'RMSE'
customRF <- list(type = "Regression", library = "randomForest", loop = NULL)
customRF$parameters <- data.frame(parameter = c("maxnodes", "ntree"), class = rep("numeric", 2), label = c(&qu...