Classes | |
class | Accuracy |
The Accuracy is a metric of performance for classification algorithms that is equal to a proportion of correctly labeled test items among all ones for given test items. More... | |
class | CVBase |
An auxiliary class for cross-validation. More... | |
class | F1 |
F1 is a metric of performance for classification algorithms that for binary classification is equal to . More... | |
class | KFoldCV |
The class KFoldCV implements k-fold cross-validation for regression and classification algorithms. More... | |
class | MetaInfoExtractor |
MetaInfoExtractor is a tool for extracting meta information about a given machine learning algorithm. More... | |
class | MSE |
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... | |
struct | NotFoundMethodForm |
class | Precision |
Precision is a metric of performance for classification algorithms that for binary classification is equal to , where and are the numbers of true positives and false positives respectively. More... | |
class | R2Score |
The R2 Score is a metric of performance for regression algorithms that represents the proportion of variance (here y) that has been explained by the independent variables in the model. More... | |
class | Recall |
Recall is a metric of performance for classification algorithms that for binary classification is equal to , where and are the numbers of true positives and false negatives respectively. More... | |
struct | SelectMethodForm |
A type function that selects a right method form. More... | |
struct | SelectMethodForm< MLAlgorithm > |
struct | SelectMethodForm< MLAlgorithm, HasMethodForm, HMFs... > |
class | SimpleCV |
SimpleCV splits data into two sets - training and validation sets - and then runs training on the training set and evaluates performance on the validation set. More... | |
struct | TrainForm |
A wrapper struct for holding a Train form. More... | |
struct | TrainForm< MT, PT, void, false, false > |
struct | TrainForm< MT, PT, void, false, true > |
struct | TrainForm< MT, PT, void, true, false > |
struct | TrainForm< MT, PT, void, true, true > |
struct | TrainForm< MT, PT, WT, false, false > |
struct | TrainForm< MT, PT, WT, false, true > |
struct | TrainForm< MT, PT, WT, true, false > |
struct | TrainForm< MT, PT, WT, true, true > |
struct | TrainFormBase4 |
struct | TrainFormBase5 |
struct | TrainFormBase6 |
struct | TrainFormBase7 |
Enumerations | |
enum | AverageStrategy { Binary , Micro , Macro } |
This enum declares possible strategies for averaging that can be used in some metrics like precision, recall, and F1. More... | |
Functions | |
template < typename DataType > | |
void | AssertSizes (const DataType &data, const arma::Row< size_t > &labels, const std::string &callerDescription) |
Assert there is the same number of the given data points and labels. More... | |
enum AverageStrategy |
This enum declares possible strategies for averaging that can be used in some metrics like precision, recall, and F1.
The "Binary" strategy means binary classification is going to be used, and there is no need to average.
Enumerator | |
---|---|
Binary | |
Micro | |
Macro |
Definition at line 25 of file average_strategy.hpp.
void mlpack::cv::AssertSizes | ( | const DataType & | data, |
const arma::Row< size_t > & | labels, | ||
const std::string & | callerDescription | ||
) |
Assert there is the same number of the given data points and labels.
data | Column-major data. |
labels | Labels. |
callerDescription | A description of the caller that can be used for error generation. |
Definition at line 29 of file facilities.hpp.