Description Usage Arguments Details Value Examples

Calculate some representative metrics for binary classification model evaluation.

1 2 3 4 5 6 7 8 9 10 | ```
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.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 | ```
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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