Select all the true statments for the decision trees: [scoring is right-wrong] a decision tree classifier can classify a data that is not linearly separable large decision trees can easily overfit to the data and have high variance | a single node decision tree (stump) has low variance and high bias decision trees find the global optima of their defined cost function, e.g. the misclassification cost

Select all the true statments for the decision trees: [scoring is right-wrong] a decision tree classifier can classify a data that is not linearly separable large decision trees can easily overfit to the data and have high variance | a single node decision tree (stump) has low variance and high bias decision trees find the global optima of their defined cost function, e.g. the misclassification cost

Database System Concepts

7th Edition

ISBN:9780078022159

Author:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan

Publisher:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan

Chapter1: Introduction

Section: Chapter Questions

Problem 1PE

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Computer Science I'm using Decision Tree to predict the majors of new university students (classify them). The data of existing students has 3 majors (classes): Science, Business, and Arts. The variables used for predictions are: Origin_City, HighSchool_MathGrade, HighSchool_SocialScienceGrade, and HighSchool_GPA. What's the length of the longest possible path from the root node to a leaf node? A) 1 B) 2 C) 3 D) 4 E) It depents on the number of unique Origin_City values.
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1Given a partially observable environment what type of planning do you suggest .Justify by giving proper technical explanation. 2Write the Alpha-Beta Algorithm. Explain Alpha cut and Beta Cut briefly. Solve the following example and show how alpha-beta pruning helped in pruning the search tree. *
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A decision tree with better splits means that: a. all the branches have binary splits. b. the tree has higher accuracy in prediction. c. the branches are of equal depth. d. the tree has more branches.
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Discretized values in a decision tree may be combined into a single branch, if: Group of answer choices, can choose multiple: order is not preserved data is continuous data has wide gaps order is preserved.
Data Mining Briefly outline the major steps of decision tree classification. Given a decision tree, you have the option of (a) converting the decision tree to rules and then pruning the resulting rules, or (b) pruning the decision tree and then converting the pruned tree to rules. What advantage does (a) have over (b)?
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