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 hyper-parameter tuning code. More...

 

Detailed Description

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.

Member Function Documentation

◆ Evaluate()

static double Evaluate ( MLAlgorithm &  model,
const DataType &  data,
const ResponsesType &  responses 
)
static

Run prediction and calculate the mean squared error.

Parameters
modelA regression model.
dataColumn-major data containing test items.
responsesGround truth (correct) target values for the test items, should be either a row vector or a column-major matrix.

Member Data Documentation

◆ NeedsMinimization

const bool NeedsMinimization = true
static

Information for hyper-parameter 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/ryan/src/mlpack.org/_src/mlpack-3.3.2/src/mlpack/core/cv/metrics/mse.hpp