Selected article for: "decision tree and forest classifier"

Author: Baati, Karim; Mohsil, Mouad
Title: Real-Time Prediction of Online Shoppers’ Purchasing Intention Using Random Forest
  • Cord-id: wnfv56gi
  • Document date: 2020_5_6
  • ID: wnfv56gi
    Snippet: In this paper, we suggest a real-time online shopper behavior prediction system which predicts the visitor’s shopping intent as soon as the website is visited. To do that, we rely on session and visitor information and we investigate naïve Bayes classifier, C4.5 decision tree and random forest. Furthermore, we use oversampling to improve the performance and the scalability of each classifier. The results show that random forest produces significantly higher accuracy and F1 Score than the comp
    Document: In this paper, we suggest a real-time online shopper behavior prediction system which predicts the visitor’s shopping intent as soon as the website is visited. To do that, we rely on session and visitor information and we investigate naïve Bayes classifier, C4.5 decision tree and random forest. Furthermore, we use oversampling to improve the performance and the scalability of each classifier. The results show that random forest produces significantly higher accuracy and F1 Score than the compared techniques.

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