41. What are tree based classifiers?
42. Suppose you are given 'n' predictions on test data by 'n' different models (M1, M2, .... Mn) respectively. Which of the following method(s) can be used to combine the predictions of these models?
Note: We are working on a regression problem
1. Median
2. Product
3. Average
4. Weighted sum
5. Minimum and Maximum
6. Generalized mean rule
Note: We are working on a regression problem
1. Median
2. Product
3. Average
4. Weighted sum
5. Minimum and Maximum
6. Generalized mean rule
43. The "curse of dimensionality" referes
44. What is Decision Tree?
45. Which one of these is a tree based learner?
46. It's possible to specify if the scaling process must include both mean and standard deviation using the parameters . . . . . . . .
47. Consider the following dataset. x,y,z are the features and T is a class(1/0). Classify the test data (0,0,1) as values of x,y,z respectively.
48. The term . . . . . . . . can be freely used, but with the same meaning adopted in physics or system theory.
49. Bootstrapping allows us to
50. What can be major issue in Leave-One-Out-Cross-Validation(LOOCV)?
Read More Section(Machine Learning)
Each Section contains maximum 100 MCQs question on Machine Learning. To get more questions visit other sections.