Consider the following dataset in which F, G and H are features and C is the class label. Explain which root node would be chosen by the entropy-based decision tree classifier described in the lecture slides. F H Maybe Yes Yes Yes Yes Yes No No No Yes No No Yes Yes Yes Yes Maybe Yes No No Yes Yes Yes Yes Maybe Yes Yes No No Yes No No Maybe Yes Yes Yes

Consider the following dataset in which F, G and H are features and C is the class label. Explain which root node would be chosen by the entropy-based decision tree classifier described in the lecture slides. F H Maybe Yes Yes Yes Yes Yes No No No Yes No No Yes Yes Yes Yes Maybe Yes No No Yes Yes Yes Yes Maybe Yes Yes No No Yes No No Maybe Yes Yes Yes

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

See similar textbooks

Similar questions

What is the main preference (inductive bias) for all decision tree learning algorithms (ID3, CART, ...)? Select one: a. We prefer a smaller tree that can "unmix" the data points on the leaf nodes. b. We prefer a high entropy state of the data points on the leaf nodes. c. We prefer a larger tree that can "unmix" the data points on the leaf nodes. d. We prefer a low entropy state of the data points on the leaf nodes.
Create a diagram (handwritten or digital) for the children of the configuration below in the game tree. Indicate which children are leaves and the outcome of the game. It is X's turn to play next. O|0| XI0|X
Use Python
What is the entropy of the system? What is the information gain of starting with attribute D in this instance?
Q4:- Perform three steps of the learning algorithm of the ART network for n=9 and m-3 assuming that the training vectors are: x1 = [1 0 0 0 1 0 0 0 1] x2 = [ 1 1 0 0 1 0 0 1 1] x3 = [10 1 0 1 0 10 1] Select an appropriate vigilance threshold p so that all three patterns are cluster separately when the sequence x1, x2, and x3 are presented .Compute the final weights of the trained network.
Explain why decision trees are often called white-box models. Give some intuitive examples for explaining it. This is very easy question. Please solve this question and give me answew in 1 hour. It's argent. Please Help Me Please
Explain what a random forest is and how it differs from decision trees in a single sentence or a few lines' worth of text. How can we be certain that the random forest method will be used with a double dose of randomization?
Develop a simple table of examples in some domain, such as classifying plants by species, and trace the construction of a decision tree by the ID3 algorithm. Construct a decision tree using py
Consider the hypothesis space defined over instances shown below, we characterize each hypothesis (apple taste) by 4-tuples. Please hand trace the ID3 classifier to build a decision tree, then predict the target value Taste=Sweet/Tart for the following instances: a) <Red, High, Some, No> b) <Red, Low, Some, Yes> c) <Yellow, Low, Some, No> d) <Green, High, None, No> e) <Green, Mid, Some, Yes> Now suppose the actual taste of the five apples above are actually “Sweet, Sweet, Sweet, Tart, Tart”, what is the accuracy of the decision tree? Please show all the steps and include the corresponding confusion matrix for accuracy calculation. (please................ as soon as give correct solution)
Why is tree pruning useful in decision tree induction? What is a drawback of using a separate set of tuples to evaluate pruning?
Draw trees for the 5 sentences below. Label all nodes. Be careful about PPs! Notice they can come either inside NP or inside VP (or both). 1. This guy returned from the Moon.2. Noam hates pizza with cheese.3. Crazy farmers are totally proud of their cows.4. I suspect [that Noam hates books].5. People from Mars asked [if Noam adores syntax].***note that (4) and (5) involve complement clauses CPs (in square brackets). Words ‘that’and ‘if’ are complementizers (Cs)***