Q18: Best running time complexity for binary search tree assuming it is already sorted? A. theta (1) B. theta(logn) C. theta(n) D. theta(2n) Q19: Which of the following is NOT in the Agile Manifesto? A. Contract negotiation over customer collaboration B. Working software over complete documentation C. Individuals and interactions over processes and tools D. Responding to change over following to plan Q20: Given a K-map with one 1 circled and the equation is ABCD (ANDed), what is it? (third circle in the first row) A. Minterm B. Maxterm 998/16 C. Prime implicant D. Essential prime implicant

Q: 8.3. Suggest a lazy version of the eager decision tree learning algorithm ID3 (see Chap- ter 3).…

A: Answer : Store instances during training phase and start building decision tree using ID3 at…

Q: Given the dataset of cars with their mileage and cost for cost prediction: Build a decision tree…

A: I have successfully built the decisiontree. See in the below steps. The reason we used decision tree…

Q: Answer the given question with a proper explanation and step-by-step solution. You are asked to…

A: This problem can be solved using a modification of the Kruskal's algorithm for finding the minimum…

Q: Would you be able to explain the "slice and dice" method for the multidimensional model?

A: What is model: A model is a simplified representation of a real-world system or concept that helps…

Q: The study of computers and computer systems. What is the key distinction between classification…

A: You have posed many queries. Let's tackle each question individually. What distinguishes…

Q: Python Question Consider two lists: seq1 = [1,2,3,4,5,6] seq2 = [1,1,2,2,3,3,4,4,5,5,6,6]…

A: I have given explanation of above answer: See below step.

Q: If you're trying to solve the Minimum Spanning Tree issue using Prim's technique, how exactly should…

A: It has been shown that the Prims Minimum Spanning Tree (MST) method is superior than Kruskal's…

Q: Write down step-by-step procedure for model estimation in NLOGIT software package?

A: Extension of LIMited DEPendent Variable Models (LIMDEP)Nested LOGIT Models (NLOGIT) – Descriptive…

Q: Both iterative deepening and bidirectional search (also known as BFS) are broken down and contrasted…

A: Given: BFS, DFS, bidirectional, and iterative Deeping are all search methods that are utilised in…

Q: Describe the Cyclomatic Complexity metric.  How does it overcome some of the limitations of the SLOC…

A: Cyclomatic Complexity is a software metric that measures the complexity of a software program by…

Q: 3. Consider the decision tree shown in Figure 2a, and the corresponding training and test sets shown…

A: Given : Here, the set of training and testing points are given. Along with this, two decision trees…

Q: How does Random Forest work? Why is it better than a single decision tree?

A: Since you have asked multiple questions, we will solve the first question for you. If you want any…

Q: Explain why a balanced search tree is better than a generic BST. Keep your discussion rigorous,…

A: Here i explain about balance and unbalance BST:…

Q: Decision trees are used to divide data into smaller groups by breaking data into two or more…

A: Introduction: Each branch of the decision tree is used to divide the data into two or more smaller…

Q: Devise a branch-and-bound algorithm for the SET COVER problem. This entails deciding: (a) What is a…

A: The SET COVER problem is an optimisation problem in which we are given a universe U of n items and a…

Q: Which method is usually used for developing decision trees? A. Left-First Breadth-First B.…

A: The goal of decision tree creation is to categorise a given amount of data based on specific…

Q: Given the dataset of cars with their mileage and cost for cost prediction: Build a decision tree…

A: Answer:- import pandas as pd import numpy as np df = np.array([['Toyota Corolla', '40',…

Q: 3. Consider the decision tree shown in Figure 2a, and the corresponding training and test sets shown…

A: The Answer is in below Steps

Q: Explain the decision tree. Illustrate the concept of a decision tree by taking a suitable example…

A: Decision Tree: A decision tree is a very specific type of probability tree that enables you to make…

Q: Show that the number of people who shake hands with an odd number of people is always even.

A:

Q: Can you describe the "slice and dice" method employed in multidimensional models?

A: In the realm of multidimensional models, the "Slice and Dice" method is a powerful technique…

Q: Given three binary features (X, Y and Z), two classes (1 a the examples listed in the table below,…

A: Sol: Since, the class varies accordingly for three times values of feature of X, therefore the…

Q: Could you describe the "slice and dice" technique for the multidimensional model

A: The "slice and dice" technique is a method used in multidimensional modeling to analyze and dissect…

Q: 1.Attached is a sample data frame of tweets data extracted between 2016-2022. I want a python code…

A: answer:-For the first task, you can utilize the pandas library to stack and control the information…

Q: In the context of Machine Learning, what is Gradient Descent Optimization? • Your choice: correct-…

A: In the context of Machine Learning, what is Gradient Descent Optimization? Answer: The iterative…

Q: • ID3 and C4.5 are heuristic algorithms that search through the space of decision trees. Why not…

A: ID3 and C4.5 are heuristic algorithms that search through the space of decision trees and why we not…

Q: 3. Consider the decision tree shown in Figure 2a, and the corresponding training and test sets shown…

A: The first two answers is given please submit the last two question separate we will answer that…

Q: In order to solve the issue of the Minimum Spanning Tree, what is the most effective approach to use…

A: Minimum Spanning Tree (MST) is a problem in graph theory that involves finding the minimum cost tree…

Q: 2)What is K- means Algorithm? Describe the use of it with a math example? 3) What is Fuzzy c-means…

A: 2) k-means is one of the simplest unsupervised learning algorithms that solve the well known…