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This set of Machine Learning (ML) Multiple Choice Questions & Answers (MCQs) focuses on Machine Learning Set 2
Q1 | Like the probabilistic view, the ________ view allows us to associate a probability of membership with each classification.
- exampler
- deductive
- classical
- inductive
Q2 | Database query is used to uncover this type of knowledge.
- deep
- hidden
- shallow
- multidimensional
Q3 | Some telecommunication company wants to segment their customers into distinct groups ,this is an example of
- supervised learning
- reinforcement learning
- unsupervised learning
- data extraction
Q4 | In the example of predicting number of babies based on stork's population ,Number of babies is
- outcome
- feature
- observation
- attribute
Q5 | Which learning Requires Self Assessment to identify patterns within data?
- unsupervised learning
- supervised learning
- semisupervised learning
- reinforced learning
Q6 | Select the correct answers for following statements.1. Filter methods are much faster compared to wrapper methods.2. Wrapper methods use statistical methods for evaluation of a subset of features while Filter methods use cross validation.
- both are true
- 1 is true and 2 is false
- both are false
- 1 is false and 2 is true
Q7 | The "curse of dimensionality" referes
- all the problems that arise when working with data in the higher dimensions, that did not exist in the lower dimensions.
- all the problems that arise when working with data in the lower dimensions, that did not exist in the higher dimensions.
- all the problems that arise when working with data in the lower dimensions, that did not exist in the lower dimensions.
- all the problems that arise when working with data in the higher dimensions, that did not exist in the higher dimensions.
Q8 | In simple term, machine learning is
- training based on historical data
- prediction to answer a query
- both a and b??
- automization of complex tasks
Q9 | If machine learning model output doesnot involves target variable then that model is called as
- descriptive model
- predictive model
- reinforcement learning
- all of the above
Q10 | Following are the descriptive models
- clustering
- classification
- association rule
- both a and c
Q11 | Different learning methods does not include?
- memorization
- analogy
- deduction
- introduction
Q12 | A measurable property or parameter of the data-set is
- training data
- feature
- test data
- validation data
Q13 | Feature can be used as a
- binary split
- predictor
- both a and b??
- none of the above
Q14 | It is not necessary to have a target variable for applying dimensionality reduction algorithms
- true
- false
Q15 | The most popularly used dimensionality reduction algorithm is Principal Component Analysis (PCA). Which of the following is/are true about PCA? 1. PCA is an unsupervised method2. It searches for the directions that data have the largest variance3. Maximum number of principal components <= number of features4. All principal components are orthogonal to each other
- 1 & 2
- 2 & 3
- 3 & 4
- all of the above
Q16 | Which of the following is a reasonable way to select the number of principal components "k"?
- choose k to be the smallest value so that at least 99% of the varinace is retained. - answer
- choose k to be 99% of m (k = 0.99*m, rounded to the nearest integer).
- choose k to be the largest value so that 99% of the variance is retained.
- use the elbow method
Q17 | Which of the folllowing is an example of feature extraction?
- construction bag of words from an email
- applying pca to project high dimensional data
- removing stop words
- forward selection
Q18 | Prediction is
- the result of application of specific theory or rule in a specific case
- discipline in statistics used to find projections in multidimensional data
- value entered in database by expert
- independent of data
Q19 | PCA works better if there is1. A linear structure in the data2. If the data lies on a curved surface and not on a flat surface3. If variables are scaled in the same unit
- 1 and 2
- 2 and 3
- 1 and 3
- 1,2 and 3
Q20 | A student Grade is a variable F1 which takes a value from A,B,C and D. Which of the following is True in the following case?
- variable f1 is an example of nominal variable
- variable f1 is an example of ordinal variable
- it doesn\t belong to any of the mentioned categories
- it belongs to both ordinal and nominal category
Q21 | What can be major issue in Leave-One-Out-Cross-Validation(LOOCV)?
- low variance
- high variance
- faster runtime compared to k-fold cross validation
- slower runtime compared to normal validation
Q22 | Imagine a Newly-Born starts to learn walking. It will try to find a suitable policy to learn walking after repeated falling and getting up.specify what type of machine learning is best suited?
- classification
- regression
- kmeans algorithm
- reinforcement learning
Q23 | Support Vector Machine is
- logical model
- proababilistic model
- geometric model
- none of the above