PyQdeck LogoPyQdeckSubjectsSyllabusBlog
Back to the 2026 paper

Module 3: Classification and Prediction

20262m

Which technique is particularly useful for reducing overfitting and obtaining a more reliable estimate of classifier performance?

(i) Cross-validation
(ii) Increasing noise
(iii) Data duplication
(iv) Removing the test set

Search GoogleAsk AI
Worked SolutionAI Assisted

Solution

Correct Answer: (i) Cross-validation

Explanation

In k-fold cross-validation the data is split into k parts (folds). The model is trained on k-1 folds and tested on the remaining fold, repeating k times so every fold is used once for testing. The average score gives a more reliable estimate of performance, and because the model is tested on unseen data each time, it helps detect and reduce overfitting.

Why other options are incorrect:

  • (ii) Increasing noise - makes the model worse.
  • (iii) Data duplication - causes test data leakage and over-optimistic results.
  • (iv) Removing the test set - removes any way to measure performance on unseen data.

Answer: (i) Cross-validation

Similar questions

MACHINE LEARNINGIn neural network. which of the following techniques is used to deal with over fitting? × (i) Dropout (ii) Regularization. (iii) Batch normalization (iv) All of the above20232mMACHINE LEARNINGWhich of the following is correct use of cross validation? (i) Selecting variables to include in a model (ii) Comparing predictors (iii) Electing parameters in prediction function (iv) All of the above20222mMACHINE LEARNINGIn Bagging, multiple models are trained on (i) The same dataset (ii) Only test data (iii) Random subsets of the training data (iv) Feature-reduced data only20252mArtificial intelligenceWhich of the following is a key step in a genetic algorithm? (i) Regression (ii) Cross-validation (iii) Crossover (iv) Tokenization20242m
PreviousA classifier correctly classifies 180 out of 200 instances. Its accuracy is (i) 80% (ii) 90% (iii) 85% (iv) 95%NextA major limitation of K-means is (i) It cannot process numerical data (ii) It always identifies outliers correctly (iii) It requires the number of clusters to be specified in advance (iv) It does not use distance measures
AI LearningWeb Platform

Coursify — Personal AI Course Generator

Struggling with a difficult subject or concept? Turn any engineering topic into a structured course with interactive lessons, quizzes, and research-backed explanations.

Try Coursify Free