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This set of Machine Learning (ML) Multiple Choice Questions & Answers (MCQs) focuses on Machine Learning Set 27
Q1 | Which of the following is true about Residuals ?
- A) Lower is better
- B) Higher is better
- C) A or B depend on the situation
- D) None of these
Q2 | Which of the following statement is true about outliers in Linear regression?
- A) Linear regression is sensitive to outliers
- B) Linear regression is not sensitive to outliers
- C) Can’t say
- D) None of these
Q3 | 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?
- A) Since the there is a relationship means our model is not good
- B) Since the there is a relationship means our model is good
- C) Can’t say
- D) None of these
Q4 | Naive Bayes classifiers are a collection ------------------of algorithms
- Classification
- Clustering
- Regression
- All
Q5 | Naive Bayes classifiers is _______________ Learning
- Supervised
- Unsupervised
- Both
- None
Q6 | Features being classified is independent of each other in Naïve Bayes Classifier
- False
- true
Q7 | Features being classified is __________ of each other in Naïve Bayes Classifier
- Independent
- Dependent
- Partial Dependent
- None
Q8 | Conditional probability is a measure of the probability of an event given that another event has already occurred.
- True
- false
Q9 | Bayes’ theorem describes the probability of an event, based on prior knowledge of conditions that might be related to the event.
- True
- false
Q10 | Bernoulli Naïve Bayes Classifier is ___________distribution
- Continuous
- Discrete
- Binary
Q11 | Multinomial Naïve Bayes Classifier is ___________distribution
- Continuous
- Discrete
- Binary
Q12 | Gaussian Naïve Bayes Classifier is ___________distribution
- Continuous
- Discrete
- Binary
Q13 | Binarize parameter in BernoulliNB scikit sets threshold for binarizing of sample features.
- True
- false
Q14 | Gaussian distribution when plotted, gives a bell shaped curve which is symmetric about the _______ of the feature values.
- Mean
- Variance
- Discrete
- Random
Q15 | SVMs directly give us the posterior probabilities P(y = 1jx) and P(y = 1jx)
- True
- false
Q16 | Any linear combination of the components of a multivariate Gaussian is a univariate Gaussian.
- True
- false
Q17 | Solving a non linear separation problem with a hard margin Kernelized SVM (Gaussian RBF Kernel) might lead to overfitting
- True
- false
Q18 | SVM is a ------------------ algorithm
- Classification
- Clustering
- Regression
- All
Q19 | SVM is a ------------------ learning
- Supervised
- Unsupervised
- Both
- None
Q20 | The linear SVM classifier works by drawing a straight line between two classes
- True
- false
Q21 | What is Model Selection in Machine Learning?
- The process of selecting models among different mathematical models, which are used to describe the same data set
- when a statistical model describes random error or noise instead of underlying relationship
- Find interesting directions in data and find novel observations/ database cleaning
- All above
Q22 | Which are two techniques of Machine Learning ?
- Genetic Programming andInductive Learning
- Speech recognition and Regression
- Both A & B
- None of the Mentioned
Q23 | Even if there are no actual supervisors ________ learning is also based on feedback provided by the environment
- Supervised
- Reinforcement
- Unsupervised
- None of the above
Q24 | When it is necessary to allow the model to develop a generalization ability and avoid a common problem called______.
- Overfitting
- Overlearning
- Classification
- Regression
Q25 | Techniques involve the usage of both labeled and unlabeled data is called___.
- Supervised
- Semi-supervised
- Unsupervised
- None of the above