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Hence, the scope of this chapter is to introduce an unsupervised learning method for the categorization of traffic scenarios In this paper, we use interpretable machine learning algorithms such as random forest and gradient boosting to find the most discriminating features for internet traffic classification. The method is based on random forests and performs the pattern recognition.

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This paper develops a realistic aid model using the random forest (rf), which is a machine learning technique Since majority of the terminologies used in random forest have already been covered in decision tree section of this chapter, let's start implementing random forest to the same toc data set covered. The model is trained and tested on simulated data from vissim traffic simulation software.

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The results show that the proposed method is an excellent approach to automatically categorize traffic scenarios, which is particularly relevant for testing autonomous vehicle functionality.