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> [!NOTE] > > See NEWS or commit history for detailed changes. ## 📚 What? ### 🚀 New features This update introduces four new features. These are described below, **Cross-Entropy Loss (PR #34 Weighted and unweighted cross-entropy loss. The function can be used as follows, ``` r # 1) define classes and # observed classes (actual) classes <- c("Class A", "Class B") actual <- factor( c("Class A", "Class B", "Class A"), levels = classes ) # 2) define probabilites # and construct response_matrix response <- c( 0.2, 0.8, 0.8, 0.2, 0.7, 0.3 ) response_matrix <- matrix( response, nrow = 3, ncol = 2, byrow = TRUE ) colnames(response_matrix) <- classes response_matrix #> Class A Class B #> [1,] 0.2 0.8 #> [2,] 0.8 0.2 #> [3,] 0.7 0.3 # 3) calculate entropy SLmetrics::entropy( actual, response_matrix ) #> [1] 1.19185 ``` **Relative Root Mean Squared Error (Commit 5521b5b49b1e268c50d6b8d61ae1c6243c4944b3):** The function normalizes the Root Mean Squared Error by a factor. There is no official way of normalizing it - and in {SLmetrics} the RMSE can be normalized using three options; mean-, range- and IQR-normalization. It can be used as follows, ```r # 1) define values actual <- rnorm(1e3) predicted <- actual + rnorm(1e3) # 2) calculate Relative Root Mean Squared Error cat( "Mean Relative Root Mean Squared Error", SLmetrics::rrmse( actual = actual, predicted = predicted, normalization = 0 ), "Range Relative Root Mean Squared Error", SLmetrics::rrmse( actual = actual, predicted = predicted, normalization = 1 ), "IQR Relative Root Mean Squared Error", SLmetrics::rrmse( actual = actual, predicted = predicted, normalization = 2 ), sep = "\n" ) #> Mean Relative Root Mean Squared Error #> 2751.381 #> Range Relative Root Mean Squared Error #> 0.1564043 #> IQR Relative Root Mean Squared Error #> 0.7323898 ``` **Weighted Receiver Operator Characteristics and Precision-Recall Curves (PR #31 These functions returns the weighted version of `TPR`, `FPR` and `precision`, `recalll` in `weighted.ROC()` and `weighted.prROC()` respectively. The `weighted.ROC()`-function[^1] can be used as follows, ```r actual <- factor(sample(c("Class 1", "Class 2"), size = 1e6, replace = TRUE, prob = c(0.7, 0.3))) response <- ifelse(actual == "Class 1", rbeta(sum(actual == "Class 1"), 2, 5), rbeta(sum(actual == "Class 2"), 5, 2)) w <- ifelse(actual == "Class 1", runif(sum(actual == "Class 1"), 0.5, 1.5), runif(sum(actual == "Class 2"), 1, **2)) ``` ``` r # Plot plot(SLmetrics::weighted.ROC(actual, response, w)) ``` <!-- --> ###⚠️ Breaking Changes - **Weighted Confusion Matix:** The `w`-argument in `cmatrix()` has been removed in favor of the more verbose weighted confusion matrix call `weighted.cmatrix()`-function. See below, Prior to version `0.3-0` the weighted confusion matrix were a part of the `cmatrix()`-function and were called as follows, ``` r SLmetrics::cmatrix( actual = actual, predicted = predicted, w = weights ) ``` This solution, although simple, were inconsistent with the remaining implementation of weighted metrics in {SLmetrics}. To regain consistency and simplicity the weighted confusion matrix are now retrieved as follows, ``` r # 1) define factors actual <- factor(sample(letters[1:3], 100, replace = TRUE)) predicted <- factor(sample(letters[1:3], 100, replace = TRUE)) weights <- runif(length(actual)) # 2) without weights SLmetrics::cmatrix( actual = actual, predicted = predicted ) ``` #> a b c #> a 7 8 18 #> b 6 13 15 #> c 15 14 4 ``` r # 2) with weights SLmetrics::weighted.cmatrix( actual = actual, predicted = predicted, w = weights ) ``` #> a b c #> a 3.627355 4.443065 7.164199 #> b 3.506631 5.426818 8.358687 #> c 6.615661 6.390454 2.233511 ### 🐛 Bug-fixes - **Return named vectors:** The classification metrics when `micro == NULL` were not returning named vectors. This has been fixed. [^1]: The syntax is the same for `weighted.prROC()`
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