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    Model Selection, Cross-Validation and Performance Metrics PYQs for GATE DA

    Solve 2+ Model Selection, Cross-Validation and Performance Metrics previous year questions for GATE DA with answers and detailed solutions. Free sample questi

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    Question 1
    2026 PYQ
    Consider that 20 stories of Author X and 10 stories of Author Y were kept together without mentioning the names of the authors. A classifier was then asked to predict the author (X or Y) of each of these stories. Let, out of X’s stories, 6 were classified as that of Y. On the other hand, out of Y’s stories, 2 were classified as that of X.

    Considering X and Y as two classes, which of the following statements is/are true?
    Question 2
    2026 PYQ
    Consider that you are training a classifier for a 10-class classification problem. Each input is represented as a 512-dimensional vector. There are 1000 samples, out of which first 100 will be used for testing. Let Leave-One-Out-Cross-Validation (LOOCV) be used for selection of the classifier model before testing.
    Which of the following options is the correct number of validation splits that will be generated?
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    Model Selection, Cross-Validation and Performance Metrics PYQs for GATE DA

    Solve 2+ Model Selection, Cross-Validation and Performance Metrics previous year questions for GATE DA with answers and detailed solutions. Free sample questions below.

    Chapter Roadmap: Model Selection, Cross-Validation and Performance Metrics

    Chapter Roadmap

    Master the art of evaluating machine learning models. By the end of this chapter, you will know how to reliably estimate generalization error and choose the best performing model.

    1. Cross-Validation and Model Selection (Part 1)
    K-Fold, LOOCV, and the bias-variance tradeoff in evaluation. Weightage: Moderate
    2. Cross-Validation and Model Selection (Part 2)
    Stratified sampling and practical model selection pipelines. Weightage: High
    3. Classification Performance Metrics (Part 1)
    Confusion matrix, Precision, Recall, and Accuracy. Weightage: Moderate
    4. Classification Performance Metrics (Part 2)
    F1 Score, ROC curves, and Area Under the Curve (AUC). Weightage: High

    Cross-Validation and Model Selection

    SUPERVISED LEARNING > MODEL SELECTION Cross-Validation and Model Selection

    Why this matters:
    A single train-test split can give a misleading estimate of model performance due to random chance. Cross-validation systematically rotates the data, providing a robust, reliable measure of how your model will generalize to unseen data.

    What you will learn here:

    • The fundamental flaw of a single holdout set.
    • The mechanics of K-Fold and Leave-One-Out Cross-Validation.
    • The bias-variance tradeoff in choosing the number of folds, K.
    • How to avoid the critical trap of data leakage during preprocessing.

    Model Selection, Cross-Validation and Performance Metrics: Solved Questions with Step-by-Step Explanations (2 Problems)

    Question 1 · Machine Learning · 2026 MSQ
    Consider that 20 stories of Author X and 10 stories of Author Y were kept together without mentioning the names of the authors. A classifier was then asked to predict the author (X or Y) of each of these stories. Let, out of X’s stories, 6 were classified as that of Y. On the other hand, out of Y’s stories, 2 were classified as that of X.

    Considering X and Y as two classes, which of the following statements is/are true?
    1. A.

      Accuracy of the classifier is 11/15.

    2. B.

      Precision of Class X is higher than the Precision of Class Y.

    3. C.

      Recall of Class X is higher than the Recall of Class Y.

    4. D.

      Accuracy of the classifier is 14/15.

    Question 2 · Machine Learning · 2026 MCQ
    Consider that you are training a classifier for a 10-class classification problem. Each input is represented as a 512-dimensional vector. There are 1000 samples, out of which first 100 will be used for testing. Let Leave-One-Out-Cross-Validation (LOOCV) be used for selection of the classifier model before testing.
    Which of the following options is the correct number of validation splits that will be generated?
    1. A.

      10

    2. B.

      512

    3. C.

      900

    4. D.

      1000

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