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Machine Learning MCQ
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- 17th Mar, 2023
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Machine Learning MCQ Questions
Machine learning is a field of computer science that deals with the problem of finding mathematical and statistical functions that best explain the relationship between input data, output data, and other inputs (external) to a system. Machine learning has some uses in areas such as detection, recommendation systems, fraud detection, machine translation, visual recognition, and the development of autonomous robotic systems.
Finally, Take the Machine Learning MCQ Test and identify your strengths in machine learning. It will help you to understand the ML concepts well & improve your knowledge.
Machine Learning MCQ with Answers
1) What is machine learning?
2) Machine Learning is a field of AI consisting of learning algorithms that ..............
3) .............. is a widely used and effective machine learning algorithm based on the idea of bagging.
4) What is the disadvantage of decision trees?
5) How can you handle missing or corrupted data in a dataset?
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6) Which of the followings are most widely used metrics and tools to assess a classification model?
7) Machine learning algorithms build a model based on sample data, known as .................
8) Machine learning is a subset of ................
9) A Machine Learning technique that helps in detecting the outliers in data.
11) What is the most significant phase in a genetic algorithm?
12) Which one in the following is not Machine Learning disciplines?
13) Machine Learning has various function representation, which of the following is not function of symbolic?
14) ................... algorithms enable the computers to learn from data, and even improve themselves, without being explicitly programmed.
15) What are the three types of Machine Learning?
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16) Which of the following is not a supervised learning?
17) Real-Time decisions, Game AI, Learning Tasks, Skill acquisition, and Robot Navigation are applications of .............
18) Which of the following is not numerical functions in the various function representation of Machine Learning?
19) Common classes of problems in machine learning is ..............
20) Which of the folloiwng clustering algorithm merges and splits nodes to help modify nonoptimal partitions?
21) Missing data items are ........................ with Bayes classifier.
22) Which supervised learning technique can process both numeric and categorical input attributes?
23) Logistic regression is a ........... regression technique that is used to model data having a ............ outcome.
24) Regression trees are often used to model which data?
25) What is called the average squared difference between classifier predicted output and actual output?
26) Data used to optimize the parameter settings of a supervised learner model is called ...............
27) Bootstrapping allows us to choose the same training instance several times.
28) The average positive difference between computed and desired outcome values
29) Which of the following statement is true about prediction problems?
30) What is the another name for an output attribute?
31) Supervised learning and unsupervised clustering both require at least one .............
32) ............ is not a machine learning algorithm.
33) Identify which is not machine learning disciplines?
34) What is the full form of PAC?
35) Analysis of Machine Learning algorithm needs ................
36) Choose the incorrect numerical functions in the various function representation of machine learning.
37) What are successful applications of Machine Learning?
38) What is called the application of machine learning methods to large databases?
39) If machine learning model output involves target variable then that model is called as predictive model.
40) ............ are the best machine learning method.
41) What is the output of training process in machine learning?
42) A model of language consists of the categories, does not include .................
43) Regression discovers causal relationships.
44) ............. is the approach of basic algorithm for decision tree induction.
45) What is the way to ensemble multiple classifications or regression?
46) What is the most common issue when using Machine Learning?
47) In Machine learning the module that must solve the given performance task is known as ...............
48) Which methods are used for the calibration in Supervised Learning?
49) How many types are available in machine learning?
50) The Bayes rule can be used in ................
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