Consider the following data points and their class labels: […
Consider the following data points and their class labels: [P1: (2, 3, 4), A], [P2: (8, 3, 4), B], [P3: (8, 4, 1), B], [P4: (6, 4, 1), A], [P5, (2, 3, 1), A]. We want to use the distance weighted 3-NN classification with this data. Each point at a distance of d has a vote of (1/d) for predicting thee class label. (a) (8) For the query point (5, 4, 4) find and report the data points in its 3-nearest-neighbor list, their weights for decision, and the class label that should be assigned to this query point. (b) (6) What is gained and what is lost when we increase the value of K in a K-NN classifier?
Read Details(10) Consider the context of selecting the best attribute fo…
(10) Consider the context of selecting the best attribute for decision tree construction. Explain briefly the difference between “information gain” and “gini ratio” metrics for selecting the best attributes. Do not write any formulas. Explain in a language only.
Read DetailsI want to create an ensemble of seven decision trees, using…
I want to create an ensemble of seven decision trees, using bagging, for a training dataset. (a) (8) Write briefly the steps you will use to generate this ensemble. (b) (4) Why do you think an ensemble generally works better than a single decision tree?
Read Details