ML Study Hub

Home / Past papers / MLP Quiz 2 / this paper

2024 Aug04: IIT M AN EXAM QDF2 - 04 Aug 2024

MLP Quiz 221 questions47 marks16 MCQ · 3 SA · 2 MSQafternoon / QDF2
Elapsed 00:00
Q1MCQ3 marks
question figure
  1. [0, 1, 1]
  2. [0, 2, 1]
  3. [0, 0, 1]
  4. [0, 0, 2]
Q2MCQ3 marks
What is ‘naive’ assumption in classifiers based on Naive Bayes?
  1. All the classes are conditionally independent of each other.
  2. All the classes are conditionally dependent on each other.
  3. All the features are conditionally dependent on each other.
  4. All the features are conditionally independent of each other.
Q3MCQ3 marks
question figure
  1. cv
  2. reg_rate
  3. alpha
  4. tol
  5. lambda
  6. None of these
Q4MCQ2 marks
What is the effect of increasing the regularization parameter alpha in Ridge Regression?
  1. It increases the complexity of the model.
  2. It reduces the complexity of the model.
  3. It has no effect on the model.
  4. It increases the model’s sensitivity to the training data.
Q5MCQ2 marks
question figure
  1. The importance of each feature in predicting the target.
  2. The residuals of the model.
  3. The intercept of the regression line.
  4. It represents the values of hyperparameters of the model.
Q6MCQ2 marks
How can we use both Ridge and Lasso Regularization in a machine learning model?
  1. By setting penalty parameter to l2
  2. By setting penalty parameter to l1
  3. By setting penalty parameter to elasticnet
  4. By setting penalty parameter to l3
Q7MCQ2 marks
Which of the following types of classification problems is being solved when a model predicts multiple labels(greater than 2) for each instance?
  1. Binary class, single label classification.
  2. Multi class, multi label classification.
  3. Binary class, multi label classification.
  4. Multi class, single label classification.
Q8MCQ2 marks
question figure
  1. The training data is split into 5 different datasets, each used to train a separatemodel.
  2. The model is trained and evaluated using 5-fold cross-validation forhyperparameter tuning
  3. Five different models are trained on 5 resampled datasets of training data.
  4. The dataset is divided into 5 parts and trained in a single pass without cross-validation.
Q9MCQ2 marks
question figure
  1. The number of correctly classified samples.
  2. The accuracy of the classifier.
  3. The loss function value.
  4. The number of misclassified samples.
Q10MCQ2 marks
question figure
  1. Trains a single Logistic Regression model for all labels.
  2. Combines Logistic Regression with another model.
  3. Trains a separate Logistic Regression model for each label.
  4. Applies Logistic Regression in a one-vs-rest strategy for multi-classclassification.
Q11MCQ2 marks
What is the main advantage of using RandomizedSearchCV over GridSearchCV ?
  1. RandomizedSearchCV is always faster.
  2. RandomizedSearchCV evaluates all possible combinations ofhyperparameters.
  3. RandomizedSearchCV can sample a larger hyperparameter space with feweriterations.
  4. RandomizedSearchCV always finds the best model.
Q12MCQ2 marks
question figure
  1. LogisticRegression
  2. Perceptron
  3. RidgeClassifier
  4. Support Vector Machine (SVM)
Q13MCQ2 marks
What type of classification problem is the Perceptron algorithm best suited for?
  1. Linearly separable binary classification problems.
  2. Non-linear classification problems.
  3. Regression problems.
  4. Unsupervised Learning Problems
Q14MCQ2 marks
question figure
  1. [0, 1, 2]
  2. [0, 1]
  3. ['setosa', 'versicolor', 'virginica']
  4. ['setosa', 'versicolor’']
  5. None of these
Q15MCQ2 marks
What is the default loss value in SGDClassifier API and it gives which classifier?
  1. ‘log loss’, LogisticRegressor
  2. ‘log loss’, LogisticClassifier
  3. ‘hinge’, SVM
  4. ‘hinge’, Perceptron
Q16MCQ2 marks
question figure
  1. 1
  2. 2
  3. 10
  4. 500
Q17SA3 marks
question figure
Q18SA3 marks
question figure
Q19SA2 marks
question figure
Q20MSQ2 marksselect all that apply
question figure
  1. ‘poly’,
  2. ‘lasso’
  3. ‘rbf’,
  4. ‘scale’
Q21MSQ2 marksselect all that apply
Which of the following scikit-learn model supports incremental learning through partial_fit method ?
  1. SGDClassifier
  2. LinearRegression
  3. Perceptron
  4. SVC

Answer key

  1. Q1 (MCQ, 3 marks): C
  2. Q2 (MCQ, 3 marks): D
  3. Q3 (MCQ, 3 marks): C
  4. Q4 (MCQ, 2 marks): B
  5. Q5 (MCQ, 2 marks): A
  6. Q6 (MCQ, 2 marks): C
  7. Q7 (MCQ, 2 marks): B
  8. Q8 (MCQ, 2 marks): B
  9. Q9 (MCQ, 2 marks): B
  10. Q10 (MCQ, 2 marks): C
  11. Q11 (MCQ, 2 marks): C
  12. Q12 (MCQ, 2 marks): C
  13. Q13 (MCQ, 2 marks): A
  14. Q14 (MCQ, 2 marks): A
  15. Q15 (MCQ, 2 marks): C
  16. Q16 (MCQ, 2 marks): C
  17. Q17 (SA, 3 marks): 0.67
  18. Q18 (SA, 3 marks): 2
  19. Q19 (SA, 2 marks): 2
  20. Q20 (MSQ, 2 marks): A, C
  21. Q21 (MSQ, 2 marks): A, C