Machine Learning Set 28

Computer Science Engineering (CSE)

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This set of Machine Learning (ML) Multiple Choice Questions & Answers (MCQs) focuses on Machine Learning Set 28

Q1 | A supervised scenario is characterized by the concept of a _____.
  • Programmer
  • Teacher
  • Author
  • Farmer
Answer: Teacher
Q2 | overlearning causes due to an excessive ______.
  • Capacity
  • Regression
  • Reinforcement
  • Accuracy
Answer: Capacity
Q3 | Which of the following are several models for feature extraction
  • regression
  • classification
  • None of the above
Answer: None of the above
Q4 | _____ provides some built-in datasets that can be used for testing purposes.
  • scikit-learn
  • classification
  • regression
  • None of the above
Answer: scikit-learn
Q5 | While using _____ all labels areturned into sequential numbers.
  • LabelEncoder class
  • LabelBinarizer class
  • DictVectorizer
  • FeatureHasher
Answer: LabelEncoder class
Q6 | _______produce sparse matrices of real numbers that can be fed into any machine learning model.
  • DictVectorizer
  • FeatureHasher
  • Both A & B
  • None of the Mentioned
Answer: Both A & B
Q7 | scikit-learn offers the class______, which is responsible for filling the holes using a strategy based on the mean, median, or frequency
  • LabelEncoder
  • LabelBinarizer
  • DictVectorizer
  • Imputer
Answer: Imputer
Q8 | Which of the following scale data by removing elements that don't belong to a given range or by considering a maximum absolute value.
  • MinMaxScaler
  • MaxAbsScaler
  • Both A & B
  • None of the Mentioned
Answer: Both A & B
Q9 | ______dataset with many features contains information proportional to the independence of all features and their variance.
  • normalized
  • unnormalized
  • Both A & B
  • None of the Mentioned
Answer: unnormalized
Q10 | The_____ parameter can assume different values which determine how the data matrix is initially processed.
  • run
  • start
  • init
  • stop
Answer: init
Q11 | ______allows exploiting the natural sparsity of data while extracting principal components.
  • SparsePCA
  • KernelPCA
  • SVD
  • init parameter
Answer: SparsePCA
Q12 | Which of the following statement is true about outliers in Linear regression?
  • Linear regression is sensitive to outliers
  • Linear regression is not sensitive to outliers
  • Can’t say
  • None of these
Answer: Linear regression is sensitive to outliers
Q13 | Suppose you plotted a scatter plot between the residuals and predicted values in linear regression and you found that there is a relationship between them. Which of the following conclusion do you make about this situation?
  • Since the there is a relationship means our model is not good
  • Since the there is a relationship means our model is good
  • Can’t say
  • None of these
Answer: Since the there is a relationship means our model is not good
Q14 | Let’s say, a “Linear regression” model perfectly fits the training data (train error is zero). Now, Which of the following statement is true?
  • You will always have test error zero
  • You can not have test error zero
  • None of the above
Answer: None of the above
Q15 | In a linear regression problem, we are using “R-squared” to measure goodness-of-fit. We add a feature in linear regression model and retrain the same model.Which of the following option is true?
  • If R Squared increases, this variable is significant.
  • If R Squared decreases, this variable is not significant.
  • Individually R squared cannot tell about variable importance. We can’t say anything about it right now.
  • None of these.
Answer: Individually R squared cannot tell about variable importance. We can’t say anything about it right now.
Q16 | To test linear relationship of y(dependent) and x(independent) continuous variables, which of the following plot best suited?
  • Scatter plot
  • Barchart
  • Histograms
  • None of these
Answer: Scatter plot
Q17 | which of the following step / assumption in regression modeling impacts the trade-off between under-fitting and over-fitting the most.
  • The polynomial degree
  • Whether we learn the weights by matrix inversion or gradient descent
  • The use of a constant-term
Answer: The polynomial degree
Q18 | Which of the following statement(s) can be true post adding a variable in a linear regression model?1. R-Squared and Adjusted R-squared both increase2. R-Squared increases and Adjusted R-squared decreases3. R-Squared decreases and Adjusted R-squared decreases4. R-Squared decreases and Adjusted R-squared increases
  • 1 and 2
  • 1 and 3
  • 2 and 4
  • None of the above
Answer: 1 and 2
Q19 | What is/are true about kernel in SVM?1. Kernel function map low dimensional data to high dimensional space2. It’s a similarity function
  • 1
  • 2
  • 1 and 2
  • None of these
Answer: 1 and 2
Q20 | Suppose you are building a SVM model on data X. The data X can be error prone which means that you should not trust any specific data point too much. Now think that you want to build a SVM model which has quadratic kernel function of polynomial degree 2 that uses Slack variable C as one of it’s hyper parameter.What would happen when you use very small C (C~0)?
  • Misclassification would happen
  • Data will be correctly classified
  • Can’t say
  • None of these
Answer: Misclassification would happen
Q21 | The cost parameter in the SVM means:
  • The number of cross-validations to be made
  • The kernel to be used
  • The tradeoff between misclassification and simplicity of the model
  • None of the above
Answer: The tradeoff between misclassification and simplicity of the model
Q22 | How do you handle missing or corrupted data in a dataset?
  • a. Drop missing rows or columns
  • b. Replace missing values with mean/median/mode
  • c. Assign a unique category to missing values
  • d. All of the above 
Answer: d. All of the above 
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