The average squared difference between classifier predicted output and actual output.
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Machine Learning
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Which of the following methods do we use to find the best fit line for data in Linear Regression?
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Following are the descriptive models
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Assume that you are given a data set and a neural network model trained on the data set. You are asked to build a decision tree model with the sole purpose of understanding/interpreting the built neural network model. In such a scenario, which among the following measures would you concentrate most on optimising?
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What are common feature selection methods in regression task?
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Regarding bias and variance, which of the following statements are true? (Here 'high' and 'low' are relative to the ideal model.
i. Models which overfit are more likely to have high bias
ii. Models which overfit are more likely to have low bias
iii. Models which overfit are more likely to have high variance
iv. Models which overfit are more likely to have low variance
i. Models which overfit are more likely to have high bias
ii. Models which overfit are more likely to have low bias
iii. Models which overfit are more likely to have high variance
iv. Models which overfit are more likely to have low variance
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Which of the following can only be used when training data are linearlyseparable?
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Wrapper methods are hyper-parameter selection methods that
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Given that we can select the same feature multiple times during the recursive partitioning of the input space, is it always possible to achieve 100% accuracy on the training data (given that we allow for trees to grow to their maximum size) when building decision trees?
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In many classification problems, the target dataset is made up of categorical labels which cannot immediately be processed by any algorithm. An encoding is needed and scikit-learn offers at least . . . . . . . . valid options
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Computers are best at learning
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what is Feature scaling done before applying K-Mean algorithm?
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Which of the following is true about Naive Bayes?
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KDD represents extraction of
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Linear Regression is a . . . . . . . . machine learning algorithm.
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The probability that a person owns a sports car given that they subscribe to automotive magazine is 40%. We also know that 3% of the adult population subscribes to automotive magazine. The probability of a person owning a sports car given that they don't subscribe to automotive magazine is 30%. Use this information to compute the probability that a person subscribes to automotive magazine given that they own a sports car
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Which among the following statements best describes our approach to learning decision trees
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Which of the following techniques would perform better for reducing dimensions of a data set?
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. . . . . . . . can be adopted when it's necessary to categorize a large amount of data with a few complete examples or when there's the need to impose some constraints to a clustering algorithm.
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Binarize parameter in BernoulliNB scikit sets threshold for binarizing of sample features.
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