Prediction Performance Metrics
Ji and Gallo's Agreement Coefficient (AC)
Accuracy
Adjusted F-score
Area Under the ROC Curve
Intercept of standardized major axis regression (SMA).
Slope of standardized major axis regression (SMA).
Modified Index of Agreement (d1).
Balanced Accuracy
Bland-Altman plot
Bookmaker Informedness
Concordance correlation coefficient (CCC)
Confusion Matrix
Critical Success Index | Jaccard's Index
Willmott's Index of Agreement (d)
Refined Index of Agreement (d1).
Distance Correlation
deltaP or Markedness
Density plot of predicted and observed values
Absolute Model Efficiency (E1)
Relative Model Efficiency (Erel)
Error rate
Fowlkes-Mallows Index
F-score
Geometric Mean
Import SQLite databases generated by APSIM NextGen
import_apsim_out
Inter-Quartile Root Mean Squared Error
Kling-Gupta Model Efficiency (KGE).
K-hat (Cohen's Kappa Coefficient)
Duveiller's Agreement Coefficient
Lack of Correlation (LCS)
Likelihood Ratios (Classification)
Mean Absolute Error (MAE)
Mean Absolute Percentage Error (MAPE)
Mean Absolute Scaled Error (MASE)
Mean Bias Error (MBE)
Matthews Correlation Coefficient | Phi Coefficient
metrica: Prediction Performance Metrics
Prediction Performance Summary
Maximal Information Coefficient
Mean Lack of Accuracy (MLA)
Mean Lack of Precision (MLP)
Mean Squared Error (MSE)
Negative Predictive Value
Nash-Sutcliffe Model Efficiency (NSE)
P4-metric
Percentage Additive Bias (PAB)
Percentage Bias Error (PBE).
Percentage Lack of Accuracy (PLA)
Percentage Lack of Precision (PLP)
Percentage Proportional Bias (PPB)
Precision | Positive Predictive Value
Prevalence
Sample Correlation Coefficient (r)
Root Mean Lack of Accuracy (RMLA)
Coefficient of determination (R2).
Robinson's Agreement Coefficient (RAC).
Relative Absolute Error (RAE)
Recall | Sensitivity | True Positive Rate | Hit rate
Relative Mean Absolute Error (RMAE)
Root Mean Lack of Precision (RMLP)
Root Mean Squared Error (RMSE)
Relative Root Mean Squared Error (RMSE)
Relative Squared Error (RSE)
Root Mean Standard Deviation Ratio (RSR)
Residual Sum of Squares (RSS)
Squared bias (SB)
Scatter plot of predicted and observed values
Squared difference between standard deviations (SDSD)
Symmetric Mean Absolute Percentage Error (SMAPE).
Specificity | Selectivity | True Negative Rate
Tiles plot of predicted and observed values
Total Sum of Squares (TSS)
Mean Bias Error Proportion (Ub)
Lack of Consistency (Uc)
Lack of Consistency (Ue)
Uncorrected Standard Deviation
Uncorrected Variance (var_u)
Accuracy Component (Xa) of CCC
A compilation of more than 80 functions designed to quantitatively and visually evaluate prediction performance of regression (continuous variables) and classification (categorical variables) of point-forecast models (e.g. APSIM, DSSAT, DNDC, supervised Machine Learning). For regression, it includes functions to generate plots (scatter, tiles, density, & Bland-Altman plot), and to estimate error metrics (e.g. MBE, MAE, RMSE), error decomposition (e.g. lack of accuracy-precision), model efficiency (e.g. NSE, E1, KGE), indices of agreement (e.g. d, RAC), goodness of fit (e.g. r, R2), adjusted correlation coefficients (e.g. CCC, dcorr), symmetric regression coefficients (intercept, slope), and mean absolute scaled error (MASE) for time series predictions. For classification (binomial and multinomial), it offers functions to generate and plot confusion matrices, and to estimate performance metrics such as accuracy, precision, recall, specificity, F-score, Cohen's Kappa, G-mean, and many more. For more details visit the vignettes <https://adriancorrendo.github.io/metrica/>.
Useful links