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For our model, the dataset of the 18 selected features for 199,771 calls was split as 75% and 25% as training and testing sets respectively. The CART Decision Tree model was chosen due to its transparent, comprehensive nature and its ability to provide the telecommunication company with ease of use. Its predictive performance is evaluated by observing the resulting confusion matrix of correctly classified vs. mis-classified cases to calculate the model¡¯s predictive accuracy (accuracy closer to 100% is preferred). The area under the Receiver Operating Characteristic Curve , commonly called AUC and ranging between 0 and 1, is also considered, with values closer to 1 being favoured. This model achieved a predictive accuracy of 89.7% and area under the Receiver Operating Characteristic Curve of 0.91, indicating acceptable performance, but with room for improvement. |
IEEE/ICACT20220012 Slide.11
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