This is a simple,comparative analysis of some of the machine learning classification algorithms,namely:
- 1.Single layer Perceptron
- 2.Decision Trees
- 3.Random Forest
- 4.Naive Bayes
- 5.Logistic Regression
- 6.A stacked ensemble,with 3 Random Forest classifiers at level 0 and Logistic Regression as the meta-classifier
The algorithms were compared on the basis of the accuarcies acheived and the two best performing algorithms- Random Forest and Stacked Ensemble were compared using the classification report and confusion matrix.
The publicly available CICIDS dataset has been used for the given analysis
All the models and the pre-processing functions have been imported from the sklearn package