Suppose you are training a linear regression model. Now consider these points.
A. both are false
B. 1 is false and 2 is true
C. 1 is true and 2 is false
D. both are true
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Gini index does not favour equal sized partitions.
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what is the function of 'Unsupervised Learning'?
A. Find clusters of the data and find low-dimensional representations of the data
B. Find interesting directions in data and find novel observations/ database cleaning
C. Interesting coordinates and correlations
D. All
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In an election, N candidates are competing against each other and people are voting for either of the candidates. Voters don't communicate with each other while casting their votes. Which of the following ensemble method works similar to above-discussed election procedure? Hint: Persons are like base models of ensemble method.
A. bagging
B. boosting
C. a or b
D. none of these
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The soft margin SVM is more preferred than the hard-margin SVM when-
A. the data is linearly seperable
B. the data is noisy and contains overlapping points
C. the data is not noisy and linearly seperable
D. the data is noisy and linearly seperable
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Which of the following option is true regarding "Regression" and "Correlation"?
A. The relationship is symmetric between x and y in both.
B. The relationship is not symmetric between x and y in both.
C. The relationship is not symmetric between x and y in case of correlation but in case of regression it is symmetric.
D. The relationship is symmetric between x and y in case of correlation but in case of regression it is not symmetric.
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A nearest neighbor approach is best used
A. with large-sized datasets.
B. when irrelevant attributes have been removed from the data.
C. when a generalized model of the data is desirable.
D. when an explanation of what has been found is of primary importance.
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For the given weather data, what is the probability that players will play if weather is sunny
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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?
A. decision trees
B. density-based clustering
C. model-based clustering
D. k-means clustering
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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
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Which of the following statement is true about outliers in Linear regression?
A. Linear regression is sensitive to outliers
B. Linear regression is not sensitive to outliers
C. Can't say
D. None of these
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Naive Bayes classifiers is . . . . . . . . Learning
A. Supervised
B. Unsupervised
C. Both
D. None
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The distance between two points calculated using Pythagoras theorem is
A. supremum distance
B. eucledian distance
C. linear distance
D. manhattan distance
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Features being classified is independent of each other in Nave Bayes Classifier
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What does learning exactly mean?
A. robots are programed so that they can perform the task based on data they gather from sensors.
B. a set of data is used to discover the potentially predictive relationship.
C. learning is the ability to change according to external stimuli and remembering most of all previous experiences.
D. it is a set of data is used to discover the potentially predictive relationship.
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What do you mean by a hard margin?
A. the svm allows very low error in classification
B. the svm allows high amount of error in classification
C. both 1 & 2
D. none of the above
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Suppose your model is demonstrating high variance across the different training sets. Which of the following is NOT valid way to try and reduce the variance?
A. increase the amount of traning data in each traning set
B. improve the optimization algorithm being used for error minimization.
C. decrease the model complexity
D. reduce the noise in the training data
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The problem of finding hidden structure in unlabeled data is called . . . . . . . .
A. supervised learning
B. unsupervised learning
C. reinforcement learning
D. none of the above
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what is the function of 'Supervised Learning'?
A. classifications, predict time series, annotate strings
B. speech recognition, regression
C. both a and b
D. none of above
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Which of the following is true about bagging?
A. 1 and 2
B. 2 and 3
C. 1 and 3
D. all of these
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