Home / Past papers / MLP Quiz 2 / this paper
IIT M DIPLOMA AN EXAM QDD4 24 Mar 2024 - 24 Mar 2024
Elapsed 00:00
Consider a binary classification dataset with labeled as 98% negative samples and 2% positive samples. A model is trained on this data, which of the following evaluation metrics are suitable for measuring effectiveness of this model:
- accuracy
- Mean Absolute Error
- smote
- F-1 score
- 0.00
- 0.50
- 0.72
- 1.00
You’re building a machine learning pipeline to preprocess data and train a model on a classification task. You decide to use a pipeline that includes data preprocessing and a support vector machine (SVM) classifier. The following code snippet demonstrates the pipeline creation and usage:
You’re building a machine learning pipeline to preprocess data and train a model on a classification task. You decide to use a pipeline that includes data preprocessing and a support vector machine (SVM) classifier. The following code snippet demonstrates the pipeline creation and usage: What is the purpose of using the pipeline in this code snippet?
You’re building a machine learning pipeline to preprocess data and train a model on a classification task. You decide to use a pipeline that includes data preprocessing and a support vector machine (SVM) classifier. The following code snippet demonstrates the pipeline creation and usage: What is the purpose of using the pipeline in this code snippet?- The pipeline combines multiple models for better model performance.
- The pipeline allows for simultaneous training of the scaler and classifier.
- The pipeline simplifies the code by encapsulating preprocessing and modelingsteps.
- The pipeline ensures that only linear SVM can be used for this classificationtask.
Which of the following is true for a hard margin SVM algorithm ?
- It does not create hyperplanes as a classification decision boundary
- It is robust to outliers
- It will correctly classify all the datapoints if the data is linearly separable.
- It is mostly used for clustering the data
Which of the following is/are correct regarding RadiusNeighborsClassifier
Which of the following is correct?
Which of the following option(s) are correct for the precision-recall curve
- A high area under the curve represents both high recall and high precision.
- The precision-recall curve shows the trade-off between precision and recall fordifferent threshold values.
- The precision-recall curve used to evaluate unsupervised algorithm forimbalanced clustered data.
- None of these
Which of the following statements are true?
- KNeighborsClassifier with low values of n_neighbors produces complexdecision boundaries.
- KNeighborsClassifier with low values of n_neighbors produces smoothdecision boundaries.
- In KNeighborsClassifier the scale of the features(columns) can impact thedecision boundaries.
- None of these
After training a multi-class classifier, you obtain the following confusion matrix. What will be the weighted average of the recall score for each class?
After training a multi-class classifier, you obtain the following confusion matrix. What will be the weighted average of the recall score for each class?
After training a multi-class classifier, you obtain the following confusion matrix. What will be the weighted average of the recall score for each class? 
Answer key
- Q1 (MCQ, 2 marks): D
- Q2 (MCQ, 2 marks): A
- Q3 (MCQ, 2 marks): D
- Q4 (MCQ, 2 marks): C
- Q5 (MCQ, 2 marks): B
- Q6 (MCQ, 2 marks): C
- Q7 (MCQ, 2 marks): C
- Q8 (MCQ, 2 marks): C
- Q9 (MCQ, 2 marks): C
- Q10 (MCQ, 2 marks): B
- Q11 (MCQ, 2 marks): D
- Q12 (MCQ, 2 marks): C
- Q13 (MSQ, 3 marks): B, C
- Q14 (MSQ, 3 marks): A, C
- Q15 (MSQ, 3 marks): A, B
- Q16 (MSQ, 3 marks): A, C
- Q17 (MSQ, 3 marks): C, D
- Q18 (MSQ, 2 marks): A, D
- Q19 (SA, 2 marks): 17
- Q20 (SA, 2 marks): 88.3 to 89.3
- Q21 (SA, 2 marks): 0.295 to 0.315
- Q22 (SA, 3 marks): 1