Optional Syntax Elements in TD_RegressionEvaluator - Analytics Database

Database Analytic Functions

Deployment
VantageCloud
VantageCore
Edition
Enterprise
IntelliFlex
VMware
Product
Analytics Database
Release Number
17.20
Published
June 2022
Language
English (United States)
Last Update
2024-04-06
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Product Category
Metrics
Specifies the list of evaluation metrics. The function returns the following metrics if the list is not provided:
Metrics Description
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'
NumOfIndependentVariables
Specifies the number of independent variables in the model.
Required with Adjusted R Squared (AR2) metric, otherwise ignored.
DegreesOfFreedom
Specifies the numerator degrees of freedom (df1) and denominator degrees of freedom (df2).
Required with FSTAT metric, otherwise ignored.