26float mse(
const VectorXf& y,
const VectorXf& yhat, VectorXf& loss,
27 const vector<float>& class_weights=vector<float>() );
38VectorXf
log_loss(
const VectorXf& y,
const VectorXf& predict_proba,
39 const vector<float>& class_weights=vector<float>());
49float mean_log_loss(
const VectorXf& y,
const VectorXf& predict_proba, VectorXf& loss,
50 const vector<float>& class_weights = vector<float>());
62 const vector<float>& class_weights=vector<float>());
72float zero_one_loss(
const VectorXf& y,
const VectorXf& predict_proba,
74 const vector<float>& class_weights=vector<float>() );
86 const vector<float>& class_weights=vector<float>() );
97VectorXf
multi_log_loss(
const VectorXf& y,
const ArrayXXf& predict_proba,
98 const vector<float>& class_weights=vector<float>());
110 const vector<float>& class_weights=vector<float>());
122 const vector<float>& class_weights=vector<float>() );
float multi_zero_one_loss(const VectorXf &y, const ArrayXXf &predict_proba, VectorXf &loss, const vector< float > &class_weights)
Accuracy for multi-classification.
float zero_one_loss(const VectorXf &y, const VectorXf &predict_proba, VectorXf &loss, const vector< float > &class_weights)
Accuracy for binary classification.
float mean_log_loss(const VectorXf &y, const VectorXf &predict_proba, VectorXf &loss, const vector< float > &class_weights)
log loss
float mean_multi_log_loss(const VectorXf &y, const ArrayXXf &predict_proba, VectorXf &loss, const vector< float > &class_weights)
Calculates the mean multinomial log loss between the predicted probabilities and the true labels.
float average_precision_score(const VectorXf &y, const VectorXf &predict_proba, VectorXf &loss, const vector< float > &class_weights)
Calculates the average precision score between the predicted probabilities and the true labels.
float mse(const VectorXf &y, const VectorXf &yhat, VectorXf &loss, const vector< float > &class_weights)
mean squared error
VectorXf multi_log_loss(const VectorXf &y, const ArrayXXf &predict_proba, const vector< float > &class_weights)
Calculates the multinomial log loss between the predicted probabilities and the true labels.
VectorXf log_loss(const VectorXf &y, const VectorXf &predict_proba, const vector< float > &class_weights)
Calculates the log loss between the predicted probabilities and the true labels.
float bal_zero_one_loss(const VectorXf &y, const VectorXf &predict_proba, VectorXf &loss, const vector< float > &class_weights)
Balanced accuracy for binary classification.
< nsga2 selection operator for getting the front
Namespace containing scoring functions for evaluation metrics.