RegressionMetric
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
*/Last updated on