Compare model performance — compare

compare_performance() compares the performance of a model with several model performance metrics.

compare_performance(model)

Arguments

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

Value

list. results of compared model performance. list has the following components:

recommend_model : character. The name of the model that is recommended as the best among the various models.
top_count : numeric. The number of best performing performance metrics by model.
mean_rank : numeric. Average of ranking individual performance metrics by model.
top_metric : list. The name of the performance metric with the best performance on individual performance metrics by model.

The performance metrics calculated are as follows.:

ZeroOneLoss : Normalized Zero-One Loss(Classification Error Loss).
Accuracy : Accuracy.
Precision : Precision.
Recall : Recall.
Specificity : Specificity.
F1_Score : F1 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.

Examples

# \donttest{
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
#> ── Checking unique value ─────────────────────────── unique value is one ──
#> No variables that unique value is one.
#> 
#> ── Checking unique rate ─────────────────────────────── high unique rate ──
#> No variables that high unique rate.
#> 
#> ── Checking character variables ─────────────────────── categorical data ──
#> No character variables.
#> 
#> 

# Run the model fitting.
result <- run_models(.data = train, target = "Kyphosis", positive = "present")

# Predict the model.
pred <- run_predict(result, test)

# Compare the model performance
compare_performance(pred)
#> Error in purrr::map(., ~future::value(.x)): ℹ In index: 1.
#> Caused by error:
#> ! object 'pred' not found
# }