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. More...
Static Public Member Functions  
template < typename MLAlgorithm , typename DataType , typename ResponsesType >  
static double  Evaluate (MLAlgorithm &model, const DataType &data, const ResponsesType &responses) 
Run prediction and calculate the mean squared error. More...  
Static Public Attributes  
static const bool  NeedsMinimization = true 
Information for hyperparameter tuning code. 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.

static 
Run prediction and calculate the mean squared error.
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. 

static 