search 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...