| MAE |
Mean absolute error (MAE) is the arithmetic average of the absolute errors between observed values and predicted values. |
| MSE |
Mean squared error (MSE) is the average of the squares of the errors between observed values and predicted values. |
| MSLE |
Mean Square Log Error (MSLE) is the relative difference between the log-transformed observed values and predicted values. |
| MAPE |
Mean Absolute Percentage Error (MAPE) is the mean or average of the absolute percentage errors of forecasts. |
| MPE |
Mean percentage error (MPE) is the computed average of percentage errors by which predicted values differ from observed values. |
| RMSE |
Root means squared error (RMSE) is the square root of the average of the squares of the errors between observed values and predicted values. |
| RMSLE |
Root means Square Log Error (RMSLE) is the square root of the relative difference between the log-transformed observed values and predicted values. |
| R2 |
R Squared (R2) is the proportion of the variation in the dependent variable that is predictable from the independent variable(s). |
| AR2 |
Adjusted R-squared (AR2) is a modified version of R-squared that has been adjusted for the independent variables in the model. |
| EV |
Explained variation (EV) measures the proportion to which a mathematical model accounts for the variation (dispersion) of a given data set. |
| ME |
Maximum-Error (ME) is the worst-case error between observed values and predicted values. |
| MPD |
Mean Poisson Deviance (MPD) is equivalent to Tweedie Deviances when the power parameter value is 1. |
| MGD |
Mean Gamma Deviance (MGD) is equivalent to Tweedie Deviances when the power parameter value is 2. |
| FSTAT |
F-statistics (FSTAT) conducts an F-test. |
| An F-test is any statistical test in which the test statistic has an F-distribution under the null hypothesis. |
| F_score |
F_score value from the F-test. |
| F_Critcialvalue |
F critical value from the F-test. (alpha, df1, df2, UPPER_TAILED) , alpha = 95% |
| P_value |
Probability value associated with the F_score value (F_score, df1, df2, UPPER_TAILED) |
| F_conclusion |
F-test result, either 'reject null hypothesis' or 'fail to reject null hypothesis'. If F_score > F_Critcialvalue, then 'reject null hypothesis' Else 'fail to reject null hypothesis' |