Ensemble Classifiers — past-year questions
AI and DS – II (ITC701) · Semester 7 · Module 5 · IT
10 past-year questions on Ensemble Classifiers have appeared in Mumbai University ITC701 papers between 2024–2026, 13 times in total. Every question is shown in full below with its marks and repeat count. Model answers require a free account.
Questions asked on Ensemble Classifiers
- 1.
Compare and contrast Bagging and Boosting with their application.
5M2× seenlast asked 2026 - 2.
Explain Metrics for evaluating classifier performance.
10M2× seenlast asked 2026 - 3.
How Bagging and Boosting handle bias-variance trade-off differently, and analyze their effectiveness in dealing with noisy data and overfitting. Explain with algorithmic such as Random Forest and AdaBoost.
10M2× seenlast asked 2025 - 4.
How does class imbalance affect classification? What are the ways to solve the class imbalance problem?
10M1× seenlast asked 2026 - 5.
Explain any 4 Metrics for evaluating classifier performance. Discuss any two cross validation methods
10M1× seenlast asked 2025 - 6.
Explain any 4 Metrics for evaluating classifier performance. Discuss on Hold out method and random sampling.
10M1× seenlast asked 2025 - 7.
Explain Ensemble Methods.
10M1× seenlast asked 2024 - 8.
Define Accuracy, precision, and recall. Evaluate performance of classifier1 and classifier 2 on the basis of above evaluation parameters, given following confusion matrix, where F = actual fraud, F' = predicted, N = actual no. fraud and N' = predicted no. fraud Classifier 1: | | F' | N' | |---|----|----| | F | 20 | 10 | | N | 10 | 60 | Classifier 2: | | F' | N' | |---|----|----| | F | 0 | 15 | | N | 5 | 80 |
10M1× seenlast asked 2024 - 9.
How do you explain random forest? Does random forest need pruning, explain in detail?
10M1× seenlast asked 2024 - 10.
Let's consider a binary classification problem where we have built a classifier to predict whether a transaction is fraudulent (positive class) or legitimate (negative class). After training the classifier and testing it on a dataset, we obtain the following confusion matrix: | | Actual Legitimate | Actual Fraudulent | |----------------------|--------------------|--------------------| | Predicted Legitimate | 850 | 30 | | Predicted Fraudulent | 20 | 100 | Calculate Accuracy, Precision, Recall and F1-score.
10M1× seenlast asked 2024
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