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Figure 4. ROC curve for Random Forest prediction. Another interesting insight has been obtained when analyzing the dependence of the prediction performance of the different methods with the amount of data available for training. We have detected that the methods actually do not need too much data to accomplish its highest performance, as it can be seen in Figure . The mean accuracy achieved by random forest (similar for logistic regression) does not significantly increase when incrementing the number of days given for training. To obtain the values in Figure 5 we have performed the validation method explained in section 2.3, and, finally, we have averaged the 48 hours predictions in single value; carrying out this process for different numbers of days of training. This result is again due to the fact that we are not incurring in overfitting (where adding more data usually reduces the overfitting problem and increases predictive performance). Figure 5. Mean prediction accuracy of random forest for a period of 48 hours vs. the number of days used for training. Doctoral Thesis: Novel applications of Machine Learning to NTAP - 102PDF Image | Novel applications of Machine Learning to Network Traffic Analysis
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