# RegressionMetric

Compute the regression metric to evaluate the quality of the predicted data compared to the true data

 double vector::RegressionMetric(    const vector&           vector_true,   // vector of true values    ENUM_REGRESSION_METRIC  metric         // metric type    );   double matrix::RegressionMetric(    const matrix&           matrix_true,   // matrix of true values    ENUM_REGRESSION_METRIC  metric         // metric type );   vector matrix::RegressionMetric(    const matrix&           matrix_true,   // matrix of true values    ENUM_REGRESSION_METRIC  metric,        // metric type    int                     axis           // axis    );

Parameters

vector_true/matrix_true

[in]  Vector or matrix of true values.

metric

[in]  Metric type from the ENUM_REGRESSION_METRIC enumeration.

axis

[in]  Axis. 0 — horizontal axis, 1 — vertical axis.

Return Value

The calculated metric which evaluates the quality of the predicted data compared to the true data.

Note

• REGRESSION_MAE — mean absolute error which represents the absolute differences between predicted values and corresponding true values
• REGRESSION_MSE — mean square error which represents the squared differences between predicted values and corresponding true values
• REGRESSION_RMSE — square root of MSE
• REGRESSION_R2 - 1 — MSE(regression) / MSE(mean)
• REGRESSION_MAPE — MAE as a percentage
• REGRESSION_MSPE — MSE as a percentage
• REGRESSION_RMSLE — RMSE computed on a logarithmic scale

Example:

 vector y_true = {3, -0.5, 2, 7};    vector y_pred = {2.5, 0.0, 2, 8}; //---    double mse=y_pred.RegressionMetric(y_true,REGRESSION_MSE);    Print("mse=",mse); //---    double mae=y_pred.RegressionMetric(y_true,REGRESSION_MAE);    Print("mae=",mae); //---    double r2=y_pred.RegressionMetric(y_true,REGRESSION_R2);    Print("r2=",r2);     /* Result    mae=0.375    mse=0.5    r2=0.9486081370449679    */