# Calculate the error evolution and final predictions of an item-weighted ensemble for rankings This function calculates the error evolution and final predictions of an item-weigthed ensemble method for ranking data (Albano et al., 2023). ```r errorevol_ranking_vector_IW(object, newdata, iw, squared = FALSE) ``` ## Arguments - `object`: an object of class 'bagging' or 'boosting' generated by the `Ensemble_ranking_IW` function. - `newdata`: a data frame that can be the same as the one used in the `object` or a new one. Continuous variables are allowed, while the dummy coding should be used for categorical variables. It must be the output of the `prep_data` function. - `iw`: a weighting vector or matrix. For coherence, `iw` should be the same vector/matrix used in `Ensemble_ranking_IW(...)`. - `squared`: logical value indicating whether squared weighting should be used in the final prediction. Default is `FALSE`. When set to `TRUE`, it corresponds to utilizing the AdaBoost.R.M3 algorithm defined by Albano et al. (2023). ## Details This function computes the error and final predictions for a boosting or bagging ranking model using item weighting. ## Returns An object of class 'errorevol'. It has two components: - **`error`**: a vector with the error values at each ensemble iteration - **`final_prediction`**: a data frame of final predictions for each observation in `newdata`. ## References Albano, A., Sciandra, M., and Plaia, A. (2023): "A weighted distance-based approach with boosted decision trees for label ranking." **Expert Systems with Applications**. Alfaro, E., Gamez, M., and Garcia, N. (2013): "adabag: An R Package for Classification with Boosting and Bagging." **Journal of Statistical Software**, Vol. 54, 2, pp. 1--35. Breiman, L. (1998): "Arcing classifiers." **The Annals of Statistics**, Vol. 26, 3, pp. 801--849. D'Ambrosio, A.[aut, cre], Amodio, S. [ctb], Mazzeo, G. [ctb], Albano, A. [ctb], Plaia, A. [ctb] (2023). ConsRank: Compute the Median Ranking(s) According to the Kemeny's Axiomatic Approach. R package version 2.1.3, https://cran.r-project.org/package=ConsRank. Freund, Y., and Schapire, R.E. (1996): "Experiments with a new boosting algorithm." In **Proceedings of the Thirteenth International Conference on Machine Learning**, pp. 148--156, Morgan Kaufmann. Plaia, A., Buscemi, S., Furnkranz, J., and Mencıa, E.L. (2021): "Comparing boosting and bagging for decision trees of rankings." **Journal of Classification**, pages 1–22. Zhu, J., Zou, H., Rosset, S., and Hastie, T. (2009): "Multi-class AdaBoost." **Statistics and Its Interface**, 2, pp. 349--360. ## Examples ```r ## Not run: # Load simulated ranking data data(simulatedRankingData) x <- simulatedRankingData$x y <- simulatedRankingData$y # Prepare the data with item weights dati <- prep_data(y, x, iw = c(2, 5, 5, 2)) # Divide the data into training and test sets set.seed(12345) samp <- sample(nrow(dati)) l <- length(dati[, 1]) sub <- sample(1:l, 2 * l / 3) data_sub1 <- dati[sub, ] data_test1 <- dati[-sub, ] # Apply ensemble ranking with AdaBoost.M1 boosting_1 <- Ensemble_ranking_IW( Label ~ ., data = data_sub1, iw = c(2, 5, 5, 2), mfinal = 3, coeflearn = "Breiman", control = rpart.control(maxdepth = 4, cp = -1), algo = "boosting", bin = FALSE ) # Evaluate the performance test_boosting1 <- errorevol_ranking_vector_IW(boosting_1, newdata = data_test1, iw=c(2,5,5,2), squared = FALSE) test_boosting1.1 <- errorevol_ranking_vector_IW(boosting_1, newdata = data_sub1, iw=c(2,5,5,2), squared = FALSE) # Plot the error evolution plot.errorevol(test_boosting1, test_boosting1.1) ## End(Not run) ```

errorevol_ranking_vector_IW function