Top 350+ Solved Machine Learning (ML) MCQ Questions Answer
Q. What are tree based classifiers?
a. classifiers which form a tree with each attribute at one level
b. classifiers which perform series of condition checking with one attribute at a time
c. both options except none
d. not possible
Q. What is gini index?
a. gini index??operates on the categorical target variables
b. it is a measure of purity
c. gini index performs only binary split
d. all (1,2 and 3)
Q. Tree/Rule based classification algorithms generate ... rule to perform the classification.
a. if-then.
b. while.
c. do while
d. switch.
Q. Decision Tree is
a. flow-chart
b. structure in which internal node represents test on an attribute, each branch represents outcome of test and each leaf node represents class label
c. both a & b
d. class of instance
Q. Which of the following is true about Manhattan distance?
a. it can be used for continuous variables
b. it can be used for categorical variables
c. it can be used for categorical as well as continuous
d. it can be used for constants
Q. A company has build a kNN classifier that gets 100% accuracy on training data. When they deployed this model on client side it has been found that the model is not at all accurate. Which of the following thing might gone wrong?Note: Model has successfully deployed and no technical issues are found at client side except the model performance
a. it is probably a overfitted model
b. ??it is probably a underfitted model
c. ??can’t say
d. wrong client data
Q. hich of the following classifications would best suit the student performance classification systems?
a. if...then... analysis
b. market-basket analysis
c. regression analysis
d. cluster analysis
Q. Which statement is true about the K-Means algorithm? Select one:
a. the output attribute must be cateogrical.
b. all attribute values must be categorical.
c. all attributes must be numeric
d. attribute values may be either categorical or numeric
Q. Which of the following can act as possible termination conditions in K-Means?1. For a fixed number of iterations.2. Assignment of observations to clusters does not change between iterations. Except for cases with a bad local minimum.3. Centroids do not change between successive iterations.4. Terminate when RSS falls below a threshold.
a. 1, 3 and 4
b. 1, 2 and 3
c. 1, 2 and 4
d. 1,2,3,4
Q. Which of the following statement is true about k-NN algorithm?1) k-NN performs much better if all of the data have the same scale2) k-NN works well with a small number of input variables (p), but struggles when the number of inputs is very large3) k-NN makes no assumptions about the functional form of the problem being solved
a. 1 and 2
b. 1 and 3
c. only 1
d. 1,2 and 3
Q. In which of the following cases will K-means clustering fail to give good results? 1) Data points with outliers 2) Data points with different densities 3) Data points with nonconvex shapes
a. 1 and 2
b. 2 and 3
c. 1, 2, and 3??
d. 1 and 3
Q. How will you counter over-fitting in decision tree?
a. by pruning the longer rules
b. by creating new rules
c. both by pruning the longer rules’ and ‘ by creating new rules’
d. over-fitting is not possible
Q. This clustering algorithm terminates when mean values computed for the current iteration of the algorithm are identical to the computed mean values for the previous iteration Select one:
a. k-means clustering
b. conceptual clustering
c. expectation maximization
d. agglomerative clustering
Q. Which one of the following is the main reason for pruning a Decision Tree?
a. to save computing time during testing
b. to save space for storing the decision tree
c. to make the training set error smaller
d. to avoid overfitting the training set
Q. You've just finished training a decision tree for spam classification, and it is getting abnormally bad performance on both your training and test sets. You know that your implementation has no bugs, so what could be causing the problem?
a. your decision trees are too shallow.
b. you need to increase the learning rate.
c. you are overfitting.
d. incorrect data