search for: postresampl

Displaying 4 results from an estimated 4 matches for "postresampl".

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2007 Dec 11
1
postResample R² and lm() R²
Hello, I'm with a conceptual doubt regarding Rsquared of both lm() and postResample(library caret). I've got a multiple regression linear model (lets say mlr) with anR² value of 67.52%. Then I use this model pro make predictions with predict() function using the same data as input , that is, use the generated model to predict the value associated with data that I used as inp...
2023 May 09
1
RandomForest tuning the parameters
...result['y'] = y_test > > result['prediction'] = predictions > > result > > > > # Import library for Metrics > > library(Metrics) > > > > print(paste0('MAE: ' , 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_...
2023 May 08
1
RandomForest tuning the parameters
...regr ? #Make prediction predictions= predict(regr, X_test) ? result= X_test result['y'] = y_test result['prediction'] = predictions result ? # Import library for Metrics library(Metrics) ? print(paste0('MAE: ' , 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(t...
2007 Dec 08
1
lm: how to calculate rsquared of the predicted values?
Hi, I've built a linear model using multiple linear regression which leads me a R-squared value of 73.58%. After that, I used this model to predicted some values based on the test data. Now I'm wondering how: 1. can I measure de R-squared value between the predicted(by the model) and real (observed) values.? 2. Measure the RMSE error . Example: suppose my data its below: REAL