Question 206 MCQ · 2.0 marks
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Machine Learning Practice · Quiz 2 · Jan 2024
Machine Learning Practice · Quiz 2 · Jan 2024
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Question 207 MCQ · 2.0 marks
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Question 208 MCQ · 2.0 marks
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
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Question 209 MCQ · 2.0 marks
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Question 210 MCQ · 2.0 marks
0.00
0.50
0.72
1.00
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Question 211 MCQ · 2.0 marks
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 modeling
steps.
The pipeline ensures that only linear SVM can be used for this classification
task.
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Question 212 MCQ · 2.0 marks
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Question 213 MCQ · 2.0 marks
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Question 214 MCQ · 2.0 marks
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Question 215 MCQ · 2.0 marks
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Question 216 MCQ · 2.0 marks
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Question 217 MCQ · 2.0 marks
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
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Question 218 MSQ · 3.0 marks
Which of the following is/are correct regarding RadiusNeighborsClassifier
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Question 219 MSQ · 3.0 marks
Which of the following is correct?
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Question 220 MSQ · 3.0 marks
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 for
different threshold values.
The precision-recall curve used to evaluate unsupervised algorithm for
imbalanced clustered data.
None of these
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Question 221 MSQ · 3.0 marks
Which of the following statements are true?
KNeighborsClassifier with low values of n_neighbors produces complex
decision boundaries.
KNeighborsClassifier with low values of n_neighbors produces smooth
decision boundaries.
In KNeighborsClassifier the scale of the features(columns) can impact the
decision boundaries.
None of these
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Question 222 MSQ · 3.0 marks
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Question 223 MSQ · 2.0 marks
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Question 224 NAT · 2.0 marks
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Question 225 NAT · 2.0 marks
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Question 226 NAT · 2.0 marks
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?
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Question 227 NAT · 3.0 marks
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