Machine Learning - Study Mode
[#136] Data used to optimize the parameter settings of a supervised learner model.
Correct Answer
(D) validation
(H) validation
[#137] In a real problem, you should check to see if the SVM is separable and then include slack variables if it is not separable.
Correct Answer
(B) FALSE
(D) FALSE
[#138] 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
Correct Answer
(B) 1 and 2
(F) 1 and 2
[#139] 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?
Correct Answer
(A) Large datasets
(E) Large datasets
[#140] This clustering algorithm initially assumes that each data instance represents a single cluster.
Correct Answer
(A) agglomerative clustering
(E) agglomerative clustering
Explanation
Solution: Agglomerative clustering is a type of hierarchical clustering technique. Initially , each data instance is considered as a separate cluster. Clusters are merged step by step based on a defined similarity measure. This process continues until all data points form a single cluster or meet a stopping criterion. Key Point: It is a bottom-up approach to clustering.