1. . . . . . . . . provides some built-in datasets that can be used for testing purposes.
2. The leaf nodes of a model tree are
3. The parameter . . . . . . . . allows specifying the percentage of elements to put into the test/training set
4. How do you handle missing or corrupted data in a dataset?
5. What is the naive assumption in a Naive Bayes Classifier.
6. Data used to optimize the parameter settings of a supervised learner model.
7. In a real problem, you should check to see if the SVM is separable and then include slack variables if it is not separable.
8. We 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
9. The 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?
10. This clustering algorithm initially assumes that each data instance represents a single cluster.
Read More Section(Machine Learning)
Each Section contains maximum 100 MCQs question on Machine Learning. To get more questions visit other sections.