4. You are given the count matrix in the table below for the attribute "Previous GPA". The class label is whether a student will pass(C1) or fail(CO) a class. We want to use the attribute "Previous GPA" as our splitting attribute in an inner node of a decision tree with a binary test condition. Which is the best way to partition the attribute values into two groups? Form the count matrices for the different partitions and compute the classification error. Explain your reasoning while considering the metric of classification error to evaluate the different partitions. Previous GPA Bad Fair Good Very good pass(C1) 6 9. 3 fail(CO) 4 4.

4. You are given the count matrix in the table below for the attribute "Previous GPA". The class label is whether a student will pass(C1) or fail(CO) a class. We want to use the attribute "Previous GPA" as our splitting attribute in an inner node of a decision tree with a binary test condition. Which is the best way to partition the attribute values into two groups? Form the count matrices for the different partitions and compute the classification error. Explain your reasoning while considering the metric of classification error to evaluate the different partitions. Previous GPA Bad Fair Good Very good pass(C1) 6 9. 3 fail(CO) 4 4.

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

Please don't use handwritting for this question How would you modify the dynamic programming algorithm for the coin collecting problem if some cells on the board are inaccessible for the robot? Apply your algorithm to the board below, where the inaccessible cells are shown by X’s. How many optimal paths are there for this board? You need to provide 1) a modified recurrence relation, 2) a pseudo code description of the algorithm, and 3) a table that stores solutions to the subproblems.
What will happen if you use a certain value for max_depth parameter in decision tree classifier?Will all leaf nodes have data from a single class in that scenario? If not, explain why this still preferred sometimes?In decision tree what do you mean by a impurity of a node? How to minimize the impurity? Do not give long answers. Just be to the point.
Determine using a tree whether the following propositions are jointly satisfiable. Ifthey are, read off from your tree a model on which they are both true (and be sure to indicate which path you are reading it off from). (Lbb∧Lda)↔∀xLcx ∃x(Lbx→Lxx)
You use your favorite decision tree algorithm to learn a decision tree for binary classification. Your tree hasJ leaves indexed j = 1, . . . , J. Leaf j contains nj training examples, mj of which are positive. However,instead of predicting a label, you would like to use this tree to predict the probability P(Y = 1 | X) (whereY is the binary class and X are the input attributes). Therefore, you decide to have each leaf predict a realvalue pj ∈ [0, 1]. -What are the values pj that yield the largest log likelihood? Show your work.
Determine using a tree whether the following propositions are jointly satisfiable. If they are, read off from your tree a model on which they are both true (and be sure to indicate which path you are reading it off from). (Lbb∧Lda)↔∀xLcx ∃x(Lbx→Lxx)
Determine using a tree whether the following propositions are jointly satisfiable. Ifthey are, read off from your tree a model on which they are both true (and be sure toindicate which path you are reading it off from).(Lbb∧Lda)↔∀xLcx ∃x(Lbx→Lxx)
Assume we have three binary input attributes, A, B, C, and one binary outcome attribute Y. The three input attributes, A, B, C take values in the set {0,1} while the Y attribute takes values in the set {True, False} a. Which attribute (A, B, or C) has the highest information gain? b. Construct the full decision tree for this problem, without doing any pruning. If at any point you have a choice between splitting on two equally desirable attributes, choose the one that comes first alphabetically. If there is a tie on how to label a leaf, then choose True.(NEED ONLY HANDWRITTEN SOLUTIION)
Write the decision tree model class that uses training accuracy to make splits from scratch. The class should be able to handle other splitting criteria, namely entropy and Gini index as well.
We have an itemset I whose support is 20. Further we know that seven of the sets that contain I also contain j. What is the confidence of the association rule I→j?
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)
You are given a dataset of continuous numerical features with a categorical target class. There is not any missing data or outliers. Which algorithm would you use to create a model? K-means clustering Logistic regression Decision Tree Linear regression
Give the sequence of S and G boundary sets computed by the CANDIDATE-ELIMINATION algorithm if it is given the sequence of training examples from Table 2.1 in reverse order. Although the final version space will be the same regardless of the sequence of examples (why?), the sets S and G computed at intermediate stages will, of course, depend on this sequence. Can you come up with ideas for ordering the training examples to minimize the sum of the sizes of these intermediate S and G sets for the H used in the EnjoySport example?