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WorksheetsMachine Learning Beginner Set - 2
Total questions: 15
Worksheet time: 11mins
How do you handle missing or corrupted data in a dataset?
Drop missing rows or columns
Replace missing values with mean/median/mode
Assign a unique category to missing values
All of the above
When performing regression or classification, which of the following is the correct way to pre-process the data?
Normalize the data → PCA →training
PCA → normalize PCA output →training
Normalize the data → PCA →normalize PCA output → training
None of the above
Supervised learning and unsupervised clustering both require at least one
Categorical Attribute
Input Attribute
Output Attribute
Hidden Attribute
A measure of goodness of fit for the estimated regression equation is the
multiple coefficient of determination
mean square of error
mean square due to regression
none of the above
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
1 and 2
2 and 3
1, 2, and 3
1 and 3
Adding more basis functions in a linear model... (pick the most probably option)
decreases model bias
decreases estimation bias
decreases variance
doesn’t affect bias and variance
Classification problems are distinguished from estimation problems in that
classification problems require the output attribute to be numeric.
classification problems require the output attribute to be categorical.
classification problems do not allow an output attribute.
classification problems are designed to predict future outcome.
Machine learning techniques differ from statistical techniques in that machine learning methods
typically assume an underlying distribution for the data.
are better able to deal with missing and noisy data.
are not able to explain their behavior.
have trouble with large-sized datasets
Suppose your model is overfitting. Which of the following is NOT a valid way to try and reduce the overfitting?
increase the amount of training data.
improve the optimization algorithm being used for error minimization.
decrease the model complexity.
reduce the noise in the training data.
The correlation coefficient for two attributes is 0.90. What does this value tell you?
the attributes are not linearly related.
as the value of one attribute increases the value of the second attribute also increases.
as the value of one attribute decreases the value of the second attribute increases.
the attributes show a curvilinear relationship.
What strategies can help reduce overfitting in decision trees?
Pruning
Enforce a minimum number of samples in leaf nodes
Enforce a maximum depth for the tree
All of the above
Which of the following are true about subset selection
Subset selection can substantially decrease the bias of support vector machines
Ridge regression frequently eliminates some of the features
Subset selection can reduce overfitting
It is impossible to find the true best subset
What is the entropy of the target variable like [0,0,0,0,0,1,0,1,1,0,1]
7/11*log(4/11)+4/11*log(7/11)
4/11*log(7/11)+4/11*log(7/11)
7/11*log(7/11)+4/11*log(4/11)
4/11*log(7/11)+7/11*log(4/11)
You’ve just finished training a random forest for spam classification, and it is getting abnormally bad performance on your validation set, but good performance on your training set. Your implementation has no bugs. What could be causing the problem?
Your decision trees are too deep
You are randomly sampling too many features when you choose a split
You have too few trees in your ensemble
all of them
Choose the correct answer suppose we have a dataset which can be trained with 100% accuracy of a Decision tree of depth 8.
Depth 5 will be high variance and low bias
Depth 2 will be low variance and low bias
Depth 6 will be low variance and high bias
Depth 10 will be high variance and high bias
