run_performance {alookr} | R Documentation |

Apply calculate performance metrics for binary classification model evaluation.

```
run_performance(model, actual = NULL)
```

`model` |
A model_df. results of predicted model that created by run_predict(). |

`actual` |
factor. A data of target variable to evaluate the model. It supports factor that has binary class. |

run_performance() is performed in parallel when calculating the performance evaluation index. However, it is not supported in MS-Windows operating system and RStudio environment.

model_df. results of predicted model. model_df is composed of tbl_df and contains the following variables.:

step : character. The current stage in the model fit process. The result of calling run_performance() is returned as "3.Performanced".

model_id : character. Type of fit model.

target : character. Name of target variable.

positive : character. Level of positive class of binary classification.

fitted_model : list. Fitted model object.

predicted : list. Predicted value by individual model. Each value has a predict_class class object.

performance : list. Calculate metrics by individual model. Each value has a numeric vector.

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.

```
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. (Case 1)
pred <- run_predict(result, test)
pred
# Calculate performace metrics. (Case 1)
perf <- run_performance(pred)
perf
perf$performance
# Predict the model. (Case 2)
pred <- run_predict(result, test[, -1])
pred
# Calculate performace metrics. (Case 2)
perf <- run_performance(pred, pull(test[, 1]))
perf
perf$performance
# Convert to matrix for compare performace.
sapply(perf$performance, "c")
```

[Package *alookr* version 0.3.9 Index]