Q. What types of tasks are strengths of machine learning algorithms in the context of Big Data?

View solution

Q. What is the primary purpose of decision tree learning?

View solution

Q. How are decision tree algorithms like ID3 and C4.5 used in the learning process?

View solution

Q. What measures are used to determine the selection of attributes for splitting in decision trees?

View solution

Q. What happens when an instance is incorrectly classified in the decision tree learning process?

View solution

Q. What is one of the improved versions of the ID3 decision tree algorithm?

View solution

Q. What does the "rxDTree" function in Revolution R* Enterprise 6.1 software provide for decision tree learning?

View solution

Q. How does data parallelism work in the context of Big Data decision trees?

View solution

Q. What is the potential drawback of using the "rxDTree" function for decision tree construction?

View solution

Q. What are decision trees converted into to improve readability?

View solution

Q. What is one way to obtain a more readable representation of a decision tree?

View solution

Q. What is the covering approach in rule induction?

View solution

Q. What is the main difference between clustering and classification?

View solution

Q. How are clusters defined in clustering?

View solution

Q. Which metric is commonly used to calculate distances in the k-means clustering algorithm?

View solution

Q. What is the challenge associated with the k-means algorithm?

View solution

Q. How can the challenge associated with the k-means algorithm be mitigated?

View solution

Q. Which category of clustering algorithms creates a nested set of clusters?

View solution

Q. What is the primary difference between agglomerative and divisive hierarchical clustering?

View solution

Q. In logistic regression, what is the dependent variable typically referred to as?

View solution