Scikit-Learn MCQ Questions And Answers - Machine Learning Libraries
Scikit-Learn MCQs : This section focuses on "basics" of Scikit-Learn. These Multiple Choice Questions (MCQ) should be practiced to improve the Scikit Learn skills required for various interviews (campus interviews, walk-in interviews, company interviews), placements, entrance exams and other competitive examinations.
1. A collection of data is called?
A. dataload
B. dataset
C. data collection
D. None of the above
View Answer
Ans : B
Explanation: A collection of data is called dataset.
2. L1 Normalisation is also called?
A. Low Absolute Deviations
B. Least Squares
C. Low Squares
D. Least Absolute Deviations
View Answer
Ans : D
Explanation: L1 Normalisation is also called Least Absolute Deviations.
3. Scikit-Learn was originally called scikits.learn
A. TRUE
B. FALSE
C. Can be true or false
D. Can not say
View Answer
Ans : A
Explanation: True, Scikit-Learn was originally called scikits.learn
4. In dataset loading, The variables of data are called its?
A. Response
B. Features
C. Target
D. Vector
View Answer
Ans : B
Explanation: Features : The variables of data are called its features. They are also known as predictors, inputs or attributes.
5. Which of the following is preprocessing technique is used when we need to convert our numerical values into Boolean values?
A. Binarized
B. Downsampling
C. Binarisation
D. Thresholding
View Answer
Ans : C
Explanation: Binarisation : This preprocessing technique is used when we need to convert our numerical values into Boolean values
6. The __________ is a regularized regression method that linearly combines both penalties i.e. L1 and L2 of the Lasso and Ridge regression methods.
A. Multi-task LASSO
B. Multi-task Elastic-Net
C. LASSO
D. Elastic-Net
View Answer
Ans : D
Explanation: The Elastic-Net is a regularized regression method that linearly combines both penalties i.e. L1 and L2 of the Lasso and Ridge regression methods. It is useful when there are multiple correlated features.
7. Which of the following are Pros of SGD?
A. Stochastic Gradient Descent (SGD) requires several hyperparameters
B. It is sensitive to feature scaling
C. Stochastic Gradient Descent (SGD) is very efficient
D. All of the above
View Answer
Ans : C
Explanation: Stochastic Gradient Descent (SGD) is very efficient are Pros of SGD.
8. Scikit-learn provides __________ classes.
A. 2
B. 3
C. 4
D. 5
View Answer
Ans : B
Explanation: Scikit-learn provides three classes namely SVC, NuSVC and LinearSVC which can perform multiclass-class classification.
9. Scikit-Learn was initially developed by David Cournapeau as a Google summer of code project in 2007.
A. Yes
B. No
C. Can be yes or no
D. Can not say
View Answer
Ans : A
Explanation: Yes, Scikit-Learn was initially developed by David Cournapeau as a Google summer of code project in 2007.
10. Which of the following are Features of Scikit Learn?
A. It is open source library and also commercially usable under BSD license
B. It is used to identify useful attributes to create supervised models
C. It is used to extract the features from data to define the attributes in image and text data
D. All of the above
View Answer
Ans : D
Explanation: All of the above are Features of Scikit Learn.
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