diff --git a/src/mlpack/core/cv/metrics/r2_score.hpp b/src/mlpack/core/cv/metrics/r2_score.hpp index b3cc2c5fad..e677f6e177 100644 --- a/src/mlpack/core/cv/metrics/r2_score.hpp +++ b/src/mlpack/core/cv/metrics/r2_score.hpp @@ -42,6 +42,9 @@ namespace cv { * where @f$ \bar{y} = frac{1}{y}\sum_{i=1}^{n} y_i @f$. * For example, a model having R2Score = 0.85, explains 85 \% variability of * the response data around its mean. + * + * @tparam AdjustedR2 If true, then the Adjusted R2 score will be used. + * Otherwise, the regular R2 score is used. */ template @@ -49,7 +52,7 @@ class R2Score { public: /** - * Run prediction and calculate the R squared or Adjusted R sauared error. + * Run prediction and calculate the R squared or Adjusted R squared error. * * @param model A regression model. * @param data Column-major data containing test items. diff --git a/src/mlpack/core/cv/metrics/r2_score_impl.hpp b/src/mlpack/core/cv/metrics/r2_score_impl.hpp index 1023f731ea..65fadc1a95 100644 --- a/src/mlpack/core/cv/metrics/r2_score_impl.hpp +++ b/src/mlpack/core/cv/metrics/r2_score_impl.hpp @@ -18,8 +18,8 @@ namespace cv { template template double R2Score::Evaluate(MLAlgorithm& model, - const DataType& data, - const ResponsesType& responses) + const DataType& data, + const ResponsesType& responses) { if (data.n_cols != responses.n_cols) { @@ -50,7 +50,8 @@ double R2Score::Evaluate(MLAlgorithm& model, return totalSumSquared ? 1.0 : DBL_MIN; // Returning adjusted R-squared. double rsq = 1 - (residualSumSquared / totalSumSquared); - return (1 - ((1 - rsq) * ((data.n_cols - 1) / (data.n_cols - data.n_rows - 1)))); + return (1 - ((1 - rsq) * ((data.n_cols - 1) / + (data.n_cols - data.n_rows - 1)))); } else { diff --git a/src/mlpack/tests/cv_test.cpp b/src/mlpack/tests/cv_test.cpp index d2bc7d7418..83c11374c5 100644 --- a/src/mlpack/tests/cv_test.cpp +++ b/src/mlpack/tests/cv_test.cpp @@ -212,7 +212,6 @@ TEST_CASE("AdjR2ScoreTest", "[CVTest]") <= 1e-7); } - /** * Test the mean squared error with matrix responses. */