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Showing content from http://accord-framework.net/docs/html/N_Accord_MachineLearning_Performance.htm below:

Base class for

methods.

Base class for performance measurement methods based on splitting the data into multiple sets, such as

,

and

.

Base class for performance measurement methods based on splitting the data into multiple sets, such as

,

and

.

Bootstrap method for generalization performance measurements (with support for stratification and default loss function for classification).

Bootstrap method for generalization performance measurements.

Bootstrap validation analysis results.

k-Fold cross-validation (with support for stratification and default loss function for classification).

k-Fold cross-validation.

k-Fold cross-validation.

Class for representing results acquired through a

.

Subset of a larger dataset.

Subset of a larger dataset.

Grid search procedure for automatic parameter tuning.

Grid search procedure for automatic parameter tuning.

Grid search procedure for automatic parameter tuning.

Grid search procedure for automatic parameter tuning.

Grid search procedure for automatic parameter tuning.

Range of parameters to be tested in a grid search.

Contains results from the grid-search procedure.

Contains results from the grid-search procedure.

Training and validation errors of a model.

Information class to store the training and validation errors of a model.

Split-Set Validation (with support for stratification and default loss function for classification).

Split-Set Validation.

Training-Validation-Testing data split.

Training-Test split.

Training-Validation-Testing data split.

Training-Validation split.

Training-Validation-Testing data split.

Training-Validation-Test split.

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