Build and test the model as the following figure. Make sure that "Test on train data" is selected. Test and Score Data Sampling Evaluation Results Cross validation Model AUC CA F1 Precision Recall...

Extracted text: Build and test the model as the following figure. Make sure that "Test on train data" is selected. Test and Score Data Sampling Evaluation Results Cross validation Model AUC CA F1 Precision Recall Datasets Test and Score Number of folds: 5 Logistic Regression 0.998 0.973 0.973 0.974 0.973 V Stratified Cross validation by feature Logistic Regression Random sampling Repeat train/test: 10 Training set size: 70 % V Stratified Model Comparison by AUC Leave one out O Test on train data Logistic Regression Test on test data Logistic Regression Target Class (Average over classes) Model Comparison Area under ROC curve O Negligible difference: Table shows probabilities that the score for the model in the row is higher than that of the model in the column.Small numbers show the probability that the difference is negligible 0.1 - 150 150 This flow follows our Machine Learning Pipeline V1.0. Dataset Evaluation Performance Training Model Check the confusion matrix Data Evaluation Results Datasets Test and Score Confusion Matrix Logistic Regression