A. The model will be rebuilt
B. The tree that caused the tie is discarded
C. One more tree is added to the forest
D. A winner is chosen at random
A. Removal of highly correlated variables to randomize the features
B. A random subset of features that are chosen at each split in the decision tree
C. Filtration of data that does not meet a pre-defined weighting thrsehold
D. The creation of out-of-bag (OOB) data that is used to select features
A. Hive
B. HBase
C. Pig
D. Mahout
A. As having a single pre-defined sentiment
B. As having a mixture of sentiments
C. As a single-predefined topic
D. As a mixture of pre-defined topics
A. Processing can be broken into smaller pieces
B. Processing in real time is required
C. Processing a large number of small files
D. Processing a small subset of data
A. Clique size and betweenness centrality
B. Eigenvector centrality and betwenness
C. Number of edges in the network and centrality measure of the cliques
D. Clique size and total number of nodes in the network
A. 90
B. 45
C. 9
D. 100
