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This set of Machine Learning (ML) Multiple Choice Questions & Answers (MCQs) focuses on Machine Learning Set 1
Q1 | Application of machine learning methods to large databases is called
- data mining.
- artificial intelligence
- big data computing
- internet of things
Q2 | If machine learning model output involves target variable then that model is called as
- descriptive model
- predictive model
- reinforcement learning
- all of the above
Q3 | In what type of learning labelled training data is used
- unsupervised learning
- supervised learning
- reinforcement learning
- active learning
Q4 | In following type of feature selection method we start with empty feature set
- forward feature selection
- backword feature selection
- both a and b??
- none of the above
Q5 | In PCA the number of input dimensiona are equal to principal components
- true
- false
Q6 | PCA can be used for projecting and visualizing data in lower dimensions.
- true
- false
Q7 | Which of the following is the best machine learning method?
- scalable
- accuracy
- fast
- all of the above
Q8 | What characterize unlabeled examples in machine learning
- there is no prior knowledge
- there is no confusing knowledge
- there is prior knowledge
- there is plenty of confusing knowledge
Q9 | What does dimensionality reduction reduce?
- stochastics
- collinerity
- performance
- entropy
Q10 | Data used to build a data mining model.
- training data
- validation data
- test data
- hidden data
Q11 | Of the Following Examples, Which would you address using an supervised learning Algorithm?
- given email labeled as spam or not spam, learn a spam filter
- given a set of news articles found on the web, group them into set of articles about the same story.
- given a database of customer data, automatically discover market segments and group customers into different market segments.
- find the patterns in market basket analysis
Q12 | Dimensionality Reduction Algorithms are one of the possible ways to reduce the computation time required to build a model
- true
- false
Q13 | You are given reviews of few netflix series marked as positive, negative and neutral. Classifying reviews of a new netflix series is an example of
- supervised learning
- unsupervised learning
- semisupervised learning
- reinforcement learning
Q14 | Which of the following is a good test dataset characteristic?
- large enough to yield meaningful results
- is representative of the dataset as a whole
- both a and b
- none of the above
Q15 | Following are the types of supervised learning
- classification
- regression
- subgroup discovery
- all of the above
Q16 | Type of matrix decomposition model is
- descriptive model
- predictive model
- logical model
- none of the above
Q17 | Following is powerful distance metrics used by Geometric model
- euclidean distance
- manhattan distance
- both a and b??
- square distance
Q18 | The output of training process in machine learning is
- machine learning model
- machine learning algorithm
- null.
- accuracy
Q19 | A feature F1 can take certain value: A, B, C, D, E, & F and represents grade of students from a college. Here feature type is
- nominal
- ordinal
- categorical
- boolean
Q20 | PCA is
- forward feature selection
- backword feature selection
- feature extraction
- all of the above
Q21 | Dimensionality reduction algorithms are one of the possible ways to reduce the computation time required to build a model.
- true
- false
Q22 | Which of the following techniques would perform better for reducing dimensions of a data set?
- removing columns which have too many missing values
- removing columns which have high variance in data
- removing columns with dissimilar data trends
- none of these
Q23 | What characterize is hyperplance in geometrical model of machine learning?
- a plane with 1 dimensional fewer than number of input attributes
- a plane with 2 dimensional fewer than number of input attributes
- a plane with 1 dimensional more than number of input attributes
- a plane with 2 dimensional more than number of input attributes