Q101
Open question. . . . . . . . provides some built-in datasets that can be used for testing purposes.
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Computer Science · all questionsMachine Learning
Machine Learning
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Q102
Open questionThe leaf nodes of a model tree are
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Q103
Open questionThe parameter . . . . . . . . allows specifying the percentage of elements to put into the test/training set
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Q104
Open questionHow do you handle missing or corrupted data in a dataset?
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Q105
Open questionWhat is the naive assumption in a Naive Bayes Classifier.
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Q106
Open questionData used to optimize the parameter settings of a supervised learner model.
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Q107
Open questionIn a real problem, you should check to see if the SVM is separable and then include slack variables if it is not separable.
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Q108
Open questionWe usually use feature normalization before using the Gaussian kernel in SVM. What is true about feature normalization?
1. We do feature normalization so that new feature will dominate other
2. Some times, feature normalization is not feasible in case of categorical variables
3. Feature normalization always helps when we use Gaussian kernel in SVM
1. We do feature normalization so that new feature will dominate other
2. Some times, feature normalization is not feasible in case of categorical variables
3. Feature normalization always helps when we use Gaussian kernel in SVM
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Q109
Open questionThe minimum time complexity for training an SVM is O(n2). According to this fact, what sizes of datasets are not best suited for SVM's?
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Q110
Open questionThis clustering algorithm initially assumes that each data instance represents a single cluster.
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Q111
Open questionHow to select best hyperparameters in tree based models?
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Q112
Open questionIf TP=9 FP=6 FN=26 TN=70 then Error rate will be
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Q113
Open questionscikit-learn also provides a class for per-sample normalization, Normalizer. It can apply . . . . . . . . to each element of a dataset
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Q114
Open questionThis supervised learning technique can process both numeric and categorical input attributes.
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Q115
Open questionChoose the correct statement with respect to 'confidence' metric in association rules
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Q116
Open questionCommon deep learning applications / problems can also be solved using . . . . . . . .
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Q117
Open questionOf the Following Examples, Which would you address using an supervised learning Algorithm?
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Q118
Open questionGiven a frequent itemset L, If |L| = k, then there are
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Q119
Open question100 people are at party. Given data gives information about how many wear pink or not, and if a man or not. Imagine a pink wearing guest leaves, was it a man?
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Q120
Open questionSuppose, you got a situation where you find that your linear regression model is under fitting the data. In such situation which of the following options would you consider?
1. I will add more variables
2. I will start introducing polynomial degree variables
3. I will remove some variables
1. I will add more variables
2. I will start introducing polynomial degree variables
3. I will remove some variables
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