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While accomplishing performance analysis in data mining basic success criteria concepts are used. These concepts ar precision, sensitivity, F-measure, and ROC criteria. During calculation of the values of said concepts, the comparison of the estimated and available data is taken into account [33]. In the comparison process, True Positive-right (TP) means True Negative-right means false (TN), False Positive-false mean: (FP), and False Negative-false means wrong (FN) values are used. Using the confusion matrix given in Figure 2, the accuracy values of the classification algorithms can be calculated. The precision statement is the ratio of the number of correct and positive estimated samples as class 1 to the number of estimated samples as class 1, as indicated in Equation (7) [34]. Sensitivity is defined as the ratio of the number of positive samples correctly classified in Equation (8) to the total number of positive samples. The F-criterion is stated as the harmonic mean of these two expressions in Equation (9) to evaluate both the sensitivity and precision expressions together [35]. The ROC value is obtained with the created curve to interpret the model performance in general. All of these performance values take values between 0 and 1.

Figure 2 While accomplishing performance analysis in data mining basic success criteria concepts are used. These concepts ar precision, sensitivity, F-measure, and ROC criteria. During calculation of the values of said concepts, the comparison of the estimated and available data is taken into account [33]. In the comparison process, True Positive-right (TP) means True Negative-right means false (TN), False Positive-false mean: (FP), and False Negative-false means wrong (FN) values are used. Using the confusion matrix given in Figure 2, the accuracy values of the classification algorithms can be calculated. The precision statement is the ratio of the number of correct and positive estimated samples as class 1 to the number of estimated samples as class 1, as indicated in Equation (7) [34]. Sensitivity is defined as the ratio of the number of positive samples correctly classified in Equation (8) to the total number of positive samples. The F-criterion is stated as the harmonic mean of these two expressions in Equation (9) to evaluate both the sensitivity and precision expressions together [35]. The ROC value is obtained with the created curve to interpret the model performance in general. All of these performance values take values between 0 and 1.

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Fig. 1. Phases of aircraft accidents
Fig. 1. Phases of aircraft accidents
TABLEI
TABLEI
Fig. 4. Accuracy values of the algorithms After creating the relationship matrix between the variables, the performance values of each algorithm were classified into these variables, and performance values analysed. These values provide important information about the general structure of the algorithm. Precision, recall, F-measure, and Roc area accuracy values of the algorithms are shown in Figure 4.
Fig. 4. Accuracy values of the algorithms After creating the relationship matrix between the variables, the performance values of each algorithm were classified into these variables, and performance values analysed. These values provide important information about the general structure of the algorithm. Precision, recall, F-measure, and Roc area accuracy values of the algorithms are shown in Figure 4.
single scatter chart where each point, as shown in Figure 2, represents an existing data. Accordingly, the relationship between each of the flight phases can be seen. Based on these analysis, the structure of each class in accidents allow researchers to create several comments on aircraft, pilots, or weather conditions. Thus, it is possible to go into more detail on the problems in aviation accidents. The plot matrix formed between determined classes for aviation accidents, accident year, the number of accidents, and death is shown in Figure 3. Fig. 3. Plot matrix showing the relationship between variables
single scatter chart where each point, as shown in Figure 2, represents an existing data. Accordingly, the relationship between each of the flight phases can be seen. Based on these analysis, the structure of each class in accidents allow researchers to create several comments on aircraft, pilots, or weather conditions. Thus, it is possible to go into more detail on the problems in aviation accidents. The plot matrix formed between determined classes for aviation accidents, accident year, the number of accidents, and death is shown in Figure 3. Fig. 3. Plot matrix showing the relationship between variables
gives better results. For this reason, it is necessary to look at the error values of the algorithms and the kappa statistical value, as shown in Figure 5. Fig. 5. Error scales of the algorithms
gives better results. For this reason, it is necessary to look at the error values of the algorithms and the kappa statistical value, as shown in Figure 5. Fig. 5. Error scales of the algorithms
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