performance_metric | R Documentation |

Calculate some representative metrics for binary classification model evaluation.

performance_metric( pred, actual, positive, metric = c("ZeroOneLoss", "Accuracy", "Precision", "Recall", "Sensitivity", "Specificity", "F1_Score", "Fbeta_Score", "LogLoss", "AUC", "Gini", "PRAUC", "LiftAUC", "GainAUC", "KS_Stat", "ConfusionMatrix"), cutoff = 0.5, beta = 1 )

`pred` |
numeric. Probability values that predicts the positive class of the target variable. |

`actual` |
factor. The value of the actual target variable. |

`positive` |
character. Level of positive class of binary classification. |

`metric` |
character. The performance metrics you want to calculate. See details. |

`cutoff` |
numeric. Threshold for classifying predicted probability values into positive and negative classes. |

`beta` |
numeric. Weight of precision in harmonic mean for F-Beta Score. |

The cutoff argument applies only if the metric argument is "ZeroOneLoss", "Accuracy", "Precision", "Recall", "Sensitivity", "Specificity", "F1_Score", "Fbeta_Score", "ConfusionMatrix".

numeric or table object. Confusion Matrix return by table object. and otherwise is numeric.: The performance metrics calculated are as follows.:

ZeroOneLoss : Normalized Zero-One Loss(Classification Error Loss).

Accuracy : Accuracy.

Precision : Precision.

Recall : Recall.

Sensitivity : Sensitivity.

Specificity : Specificity.

F1_Score : F1 Score.

Fbeta_Score : F-Beta Score.

LogLoss : Log loss / Cross-Entropy Loss.

AUC : Area Under the Receiver Operating Characteristic Curve (ROC AUC).

Gini : Gini Coefficient.

PRAUC : Area Under the Precision-Recall Curve (PR AUC).

LiftAUC : Area Under the Lift Chart.

GainAUC : Area Under the Gain Chart.

KS_Stat : Kolmogorov-Smirnov Statistic.

ConfusionMatrix : Confusion Matrix.

library(dplyr) # Divide the train data set and the test data set. sb <- rpart::kyphosis %>% split_by(Kyphosis) # Extract the train data set from original data set. train <- sb %>% extract_set(set = "train") # Extract the test data set from original data set. test <- sb %>% extract_set(set = "test") # Sampling for unbalanced data set using SMOTE(synthetic minority over-sampling technique). train <- sb %>% sampling_target(seed = 1234L, method = "ubSMOTE") # Cleaning the set. train <- train %>% cleanse # Run the model fitting. result <- run_models(.data = train, target = "Kyphosis", positive = "present") result # Predict the model. pred <- run_predict(result, test) pred # Calculate Accuracy. performance_metric(attr(pred$predicted[[1]], "pred_prob"), test$Kyphosis, "present", "Accuracy") # Calculate Confusion Matrix. performance_metric(attr(pred$predicted[[1]], "pred_prob"), test$Kyphosis, "present", "ConfusionMatrix") # Calculate Confusion Matrix by cutoff = 0.55. performance_metric(attr(pred$predicted[[1]], "pred_prob"), test$Kyphosis, "present", "ConfusionMatrix", cutoff = 0.55)

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