Top 350+ Solved Machine Learning (ML) MCQ Questions Answer

From 376 to 390 of 422

Q. The SVM’s are less effective when:

a. the data is line

b. the data is cl

  • c. the data is noisy and contains

Q. Suppose you are using RBF kernel in SVM with high Gamma valu

a. the model wo

b. uthe model wo

c. the model wou

d. none of the ab

  • b. uthe model wo

Q. The cost parameter in the SVM means:

a. the number of cross- validations to be made

b. the kernel to be used

c. the tradeoff between misclassificati on and simplicity ofthe model

d. none of the above

  • c. the tradeoff between misclassificati on and simplicity ofthe model

Q. If I am using all features of my dataset and I achieve 100% accura

a. underfitting

b. nothing, the m

  • c. overfitting

Q. Which of the following are real world applications of the SVM?

a. text and hype

b. image classifi

c. clustering of n

d. all of the abov

  • d. all of the abov

Q. We usually use feature normalization before using the Gaussian k

a. e 1

b. 1 and 2

c. 1 and 3

d. 2 and 3

  • b. 1 and 2

Q. In reinforcement learning, this feedback is usually called as .

a. overfitting

b. overlearning

c. reward

d. none of above

  • c. reward

Q. In the last decade, many researchers started trainingbigger and bigger models, built with several different layers that's why this approach is called .

a. deep learning

b. machine learning

c. reinforcement learning

d. unsupervised learning

  • a. deep learning

Q. When it is necessary to allow the model to develop a generalization ability and avoid a common problemcalled .

a. overfitting

b. overlearning

c. classification

d. regression

  • a. overfitting

Q. Techniques involve the usage of both labeled and unlabeled data is called .

a. supervised

b. semi- supervised

c. unsupervised

d. none of the above

  • b. semi- supervised

Q. Reinforcement learning is particularly efficient when .

a. the environment is not completely deterministic

b. it\s often very dynamic

c. it\s impossible to have a precise error measure

d. all above

  • d. all above
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