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WorksheetsDS321 English
Total questions: 20
Worksheet time: 10mins
High entropy means that the partitions in classification are
pure
not pure
useful
useless
A machine learning problem involves four attributes plus a class. The attributes have 3, 2, 2, and 2 possible values each. The class has 3 possible values. How many maximum possible different examples are there?
12
24
48
72
Which of the following is NOT supervised learning?
PCA
Decision Tree
Linear Regression
Naive Bayesian
Which of the following statements about Naive Bayes is incorrect?
Attributes are equally important.
Attributes are statistically dependent of one another given the class value.
Attributes are statistically independent of one another given the class value
Attributes can be nominal or numeric
Suppose we would like to perform clustering on spatial data such as the geometrical locations of houses. We wish to produce clusters of many different sizes and shapes. Which of the following methods is the most appropriate?
Decision Trees
Density-based clustering
Model-based clustering
K-means clustering
Which of the following are the spatial clustering algorithms?
Partitioning based clustering
K-means clustering
Grid based clustering
All of the above
Which of the following tasks can be best solved using Clustering.
Predicting the amount of rainfall based on various cues
Detecting fraudulent credit card transactions
Training a robot to solve a maze
All of the above
Compared to the variance of the Maximum Likelihood Estimate (MLE), the variance of the Maximum A Posteriori (MAP) estimate is ________
higher
same
lower
it could be any of the above
Choose the correct option(s) from the following
When working with a small dataset, one should prefer low bias/high variance classifiers over high bias/low variance classifiers.
When working with a small dataset, one should prefer high bias/low variance classifiers over low bias/high variance classifiers.
When working with a large dataset, one should prefer high bias/low variance classifiers over low bias/high variance classifiers.
When working with a large dataset, one should prefer high bias/high variance classifiers over high bias/low variance classifiers.
Predicting the amount of rainfall in a region based on various cues is a ______ problem.
Supervised learning
Unsupervised learning
Clustering
None of the above
A and B are two events. If P(A, B) decreases while P(A) increases, which of the following is true?
P(A|B) decreases
P(B|A) decreases
P(B) decreases
All of above
In building a linear regression model for a particular data set, you observe the coefficient of one of the features having a relatively high negative value. This suggests that
This feature has a strong effect on the model (should be retained)
This feature does not have a strong effect on the model (should be ignored)
It is not possible to comment on the importance of this feature without additional information
Nothing can be determined.
MLE estimates are often undesirable because
they are biased
they have high variance
they are not consistent estimators
None of the above
Suppose we like to calculate P(H|E, F) and we have no conditional independence information. Which of the following sets of numbers are sufficient for the calculation?
P(E, F), P(H), P(E|H), P(F|H)
P(E, F), P(H), P(E, F|H)
P(H), P(E|H), P(F|H)
P(E, F), P(E|H), P(F|H)
Suppose you are given an EM algorithm that finds maximum likelihood estimates for a model with latent variables. You are asked to modify the algorithm so that it finds MAP estimates instead. Which step or steps do you need to modify?
Expectation
Maximization
No modification necessary
Both
MLE estimates are often undesirable because
they are biased
they have high variance
they are not consistent estimators
None of the above
Which of the following is a clustering algorithm in machine learning?
Expectation Maximization
CART
Gaussian Naïve Bayes
Apriori
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?
Your decision trees are too shallow.
You need to increase the learning rate.
You are overfitting.
None of the above
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?
Your decision trees are too shallow.
You need to increase the learning rate.
You are overfitting.
None of the above.
N-grams are defined as the combination of N keywords together. How many bi-grams can be generated from the given sentence: Gandhiji is the father of our nation
7
6
8
9
