It depends on the type of data: categorical or numeric. You can therefore choose Chi squared, Mutual Information or correlation according to your target

To examine the performance disparity across classifiers, you could do statistical tests such as ANOVA (Analysis of Variance) or paired t-tests.

Pairwise t-tests can determine which distinct classifiers have significantly different performance.

So, to check why Random Forest performs better, I implemented this in Python using the breast_cancer dataset from sklearn, which you may use in your IDS scenario.

Also, I used the accuracy metric for determining the performance of each model.

#import all this libraries

from sklearn.datasets import load_breast_cancer

from sklearn.model_selection import cross_val_score

from sklearn.ensemble import RandomForestClassifier, BaggingClassifier, AdaBoostClassifier, StackingClassifier

from sklearn.tree import DecisionTreeClassifier

from scipy.stats import ttest_rel

#load the dataset

data = load_breast_cancer()

X = data.data

y = data.target

# Initialize the classifiers

rforest = RandomForestClassifier()

bagging = BaggingClassifier(estimator=DecisionTreeClassifier())

boosting = AdaBoostClassifier(estimator=DecisionTreeClassifier())

stacking = StackingClassifier(estimators=[('rforest', rforest), ('bagging', bagging), ('boosting', boosting)], final_estimator=DecisionTreeClassifier())

# Train and evaluate models using cross-validation

rforest_scores = cross_val_score(rforest, X, y, cv=5, scoring='accuracy')

bagging_scores = cross_val_score(bagging, X, y, cv=5, scoring='accuracy')

boosting_scores = cross_val_score(boosting, X, y, cv=5, scoring='accuracy')

stacking_scores = cross_val_score(stacking, X, y, cv=5, scoring='accuracy')

# Perform paired t-tests

t_stat, rforest_bagging_pvalue = ttest_rel(rforest_scores, bagging_scores)

t_stat, rforest_boosting_pvalue = ttest_rel(rforest_scores, boosting_scores)

t_stat, rforest_stacking_pvalue = ttest_rel(rforest_scores, stacking_scores)

# Print p-values

print("Paired t-test p-values (Random Forest vs. Bagging):", rforest_bagging_pvalue)

print("Paired t-test p-values (Random Forest vs. Boosting):", rforest_boosting_pvalue)

print("Paired t-test p-values (Random Forest vs. Stacking):", rforest_stacking_pvalue)

#check if the difference in accuracy between the ensemble methods is statistically significant

if rforest_bagging_pvalue < 0.05:

print('The difference in accuracy between Random Forest vs. Bagging is statistically significant\n')

else:

print('The difference in accuracy between Random Forest vs. Bagging is not statistically significant\n')

if rforest_boosting_pvalue < 0.05:

print('The difference in accuracy between Random Forest vs. Boosting is statistically significant\n')

else:

print('The difference in accuracy between Random Forest vs. Boosting is not statistically significant\n')

if rforest_stacking_pvalue < 0.05:

print('The difference in accuracy between Random Forest vs. Stacking is statistically significant\n')

else:

print('The difference in accuracy between Random Forest vs. Stacking is not statistically significant\n')

I hope this one helps.

Sani Sufyan Bature Thank you very much for the wonderful answer. So, if the output is "The difference in accuracy between Random Forest vs. Bagging is statistically significant", mean Random Forest is better than Bagging ??

You’re welcome.

Yes, I think so. Because it suggests that random forest genuinely performs better than bagging with a high level of confidence in this context. Even though I can’t say generally, in order to generalize it, we need to check on different and diverse datasets.