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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
Q2 | If machine learning model output involves target variable then that model is called as
Q3 | In what type of learning labelled training data is used
Q4 | In following type of feature selection method we start with empty feature set
Q5 | In PCA the number of input dimensiona are equal to principal components
Q6 | PCA can be used for projecting and visualizing data in lower dimensions.
Q7 | Which of the following is the best machine learning method?
Q8 | What characterize unlabeled examples in machine learning
Q9 | What does dimensionality reduction reduce?
Q10 | Data used to build a data mining model.
Q11 | Of the Following Examples, Which would you address using an supervised learning Algorithm?
Q12 | Dimensionality Reduction Algorithms are one of the possible ways to reduce the computation time required to build a model
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
Q14 | Which of the following is a good test dataset characteristic?
Q15 | Following are the types of supervised learning
Q16 | Type of matrix decomposition model is
Q17 | Following is powerful distance metrics used by Geometric model
Q18 | The output of training process in machine learning is
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
Q20 | PCA is
Q21 | Dimensionality reduction algorithms are one of the possible ways to reduce the computation time required to build a model.
Q22 | Which of the following techniques would perform better for reducing dimensions of a data set?
Q23 | What characterize is hyperplance in geometrical model of machine learning?