Top 350+ Solved Machine Learning (ML) MCQ Questions Answer
Q. Bootstrapping allows us to
a. choose the same training instance several times.
b. choose the same test set instance several times.
c. build models with alternative subsets of the training data several times.
d. test a model with alternative subsets of the test data several times.
Q. The correlation coefficient for two real-valued attributes is –0.85. What does this value tell you?
a. the attributes are not linearly related.
b. as the value of one attribute increases the value of the second attribute also increases.
c. as the value of one attribute decreases the value of the second attribute increases.
d. the attributes show a curvilinear relationship.
Q. The average squared difference between classifier predicted output and actual output.
a. mean squared error
b. root mean squared error
c. mean absolute error
d. mean relative error
Q. Simple regression assumes a __________ relationship between the input attribute and outputattribute.
a. linear
b. quadratic
c. reciprocal
d. inverse
Q. Regression trees are often used to model _______ data.
a. linear
b. nonlinear
c. categorical
d. symmetrical
Q. The leaf nodes of a model tree are
a. averages of numeric output attribute values.
b. nonlinear regression equations.
c. linear regression equations.
d. sums of numeric output attribute values.
Q. Logistic regression is a ________ regression technique that is used to model data having a_____outcome.
a. linear, numeric
b. linear, binary
c. nonlinear, numeric
d. nonlinear, binary
Q. This technique associates a conditional probability value with each data instance.
a. linear regression
b. logistic regression
c. simple regression
d. multiple linear regression
Q. This supervised learning technique can process both numeric and categorical input attributes.
a. linear regression
b. bayes classifier
c. logistic regression
d. backpropagation learning
Q. With Bayes classifier, missing data items are
a. treated as equal compares.
b. treated as unequal compares.
c. replaced with a default value.
d. ignore
Q. This clustering algorithm merges and splits nodes to help modify nonoptimal partitions.
a. agglomerative clustering
b. expectation maximization
c. conceptual clustering
d. k-means clustering
Q. This clustering algorithm initially assumes that each data instance represents a single cluster.
a. agglomerative clustering
b. conceptual clustering
c. k-means clustering
d. expectation maximization
Q. This unsupervised clustering algorithm terminates when mean values computed for the currentiteration of the algorithm are identical to the computed mean values for the previous iteration.
a. agglomerative clustering
b. conceptual clustering
c. k-means clustering
d. expectation maximization
Q. Machine learning techniques differ from statistical techniques in that machine learning methods
a. typically assume an underlying distribution for the dat
b. are better able to deal with missing and noisy data.
c. are not able to explain their behavior.
d. have trouble with large-sized datasets.
Q. In reinforcement learning if feedback is negative one it is defined as____.
a. Penalty
b. Overlearning
c. Reward
d. None of above