The MeanSquaredError is a metric of performance for regression algorithms that is equal to the mean squared error between predicted values and ground truth (correct) values for given test items.
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template 
static double  Evaluate (MLAlgorithm &model, const DataType &data, const ResponsesType &responses) 
 Run prediction and calculate the mean squared error. More...


The MeanSquaredError is a metric of performance for regression algorithms that is equal to the mean squared error between predicted values and ground truth (correct) values for given test items.
Definition at line 25 of file mse.hpp.
◆ Evaluate()
static double Evaluate 
( 
MLAlgorithm & 
model, 


const DataType & 
data, 


const ResponsesType & 
responses 

) 
 

static 
Run prediction and calculate the mean squared error.
 Parameters

model  A regression model. 
data  Columnmajor data containing test items. 
responses  Ground truth (correct) target values for the test items, should be either a row vector or a columnmajor matrix. 
◆ NeedsMinimization
const bool NeedsMinimization = true 

static 
Information for hyperparameter tuning code.
It indicates that we want to minimize the measurement.
Definition at line 45 of file mse.hpp.
The documentation for this class was generated from the following file:
 /home/jenkinsmlpack/mlpack.org/_src/mlpack3.4.2/src/mlpack/core/cv/metrics/mse.hpp