Q321
Open questionFollowing are the types of supervised learning
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Computer Science · all questionsMachine Learning
Machine Learning
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Q322
Open questionoverlearning causes due to an excessive . . . . . . . .
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Q323
Open questionYou trained a binary classifier model which gives very high accuracy on the training data, but much lower accuracy on validation data. Which is false.
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Q324
Open questionFor the given weather data, Calculate probability of playing
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Q325
Open questionWhich of the following is an example of a deterministic algorithm?
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Q326
Open questionGaussian Nave Bayes Classifier is . . . . . . . . distribution
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Q327
Open questionIn which of the following each categorical label is first turned into a positive integer and then transformed into a vector where only one feature is 1 while all the others are 0.
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Q328
Open questionSimple regression assumes a . . . . . . . . relationship between the input attribute and output attribute.
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Q329
Open questionThe . . . . . . . . step eliminates the extensions of (k-1)-itemsets which are not found to be frequent,from being considered for counting support
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Q330
Open questionWhich of the following is true about weighted majority votes?
1. We want to give higher weights to better performing models
2. Inferior models can overrule the best model if collective weighted votes for inferior models is higher than best model
3. Voting is special case of weighted voting
1. We want to give higher weights to better performing models
2. Inferior models can overrule the best model if collective weighted votes for inferior models is higher than best model
3. Voting is special case of weighted voting
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Q331
Open questionThis clustering algorithm merges and splits nodes to help modify nonoptimal partitions.
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Q332
Open questionFollowing is powerful distance metrics used by Geometric model
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Q333
Open questionIf I am using all features of my dataset and I achieve 100% accuracy on my training set, but ~70% on validation set, what should I look out for?
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Q334
Open questionHow does number of observations influence overfitting? Choose the correct answer(s).Note: Rest all parameters are same
1. In case of fewer observations, it is easy to overfit the data.
2. In case of fewer observations, it is hard to overfit the data.
3. In case of more observations, it is easy to overfit the data.
4. In case of more observations, it is hard to overfit the data.
1. In case of fewer observations, it is easy to overfit the data.
2. In case of fewer observations, it is hard to overfit the data.
3. In case of more observations, it is easy to overfit the data.
4. In case of more observations, it is hard to overfit the data.
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Q335
Open questionThe . . . . . . . . parameter can assume different values which determine how the data matrix is initially processed.
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Q336
Open questionSuppose you have trained an SVM with linear decision boundary after training SVM, you correctly infer that your SVM model is under fitting. Which of the following is best option would you more likely to consider iterating SVM next time?
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Q337
Open questionHow many coefficients do you need to estimate in a simple linear regression model (One independent variable)?
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Q338
Open questionA regression model in which more than one independent variable is used to predict the dependent variable is called
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Q339
Open questionIf you use an ensemble of different base models, is it necessary to tune the hyper parameters of all base models to improve the ensemble performance?
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Q340
Open questionSVM algorithms use a set of mathematical functions that are defined as the kernel.
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