Supposed you are building random forest model using pruned ID3 decision trees, which trees, which split a node on the attribute, that has the highest information gain. Based on the following 4 scenarious which option has the highest information gain? * Option A * Option B *Option C *Option D *Option B&

Q: You need to build a tree-based multi-class classifier F for 16 classes: C1-16. a. Show how you can…

A: Answer: We have to build multi classifier F for 16 classes and we have to find or show how to build…

Q: In decision tree regression how does the leaf predict the numeric value in a decision tree…

A: Decision Trees (DTs) are a non-parametric supervised learning method used for classification and…

Q: This is a Decision Tree Classification Model Using Entropy Information Gain. What * ?is the depth of…

A: The depth of a decision tree is the length of the path from a root to a leaf.

Q: How many possible Nearest-Neighbor Interchanges can be performed on the tree below? Draw and label…

A:

Q: 4. Consider a node, t, in a decision tree. The set of training records in node t is Dt, which…

A: Dear Student, Formulae for finding Misclassification error = 1-max(p,1-p)    //for two classes where…

Q: on between potential overfitting of a decision tree model and the minimum entropy required to…

A: given - Explain the connection between potential overfitting of a decision tree model and the…

Q: What is the smallest possible depth of a leaf node in a decision tree for a comparison sort of n…

A: Note:  As per the Bartleby guideline I can only solve the first question. Please post the rest of…

Q: Based on the below table, use information gain to build a decision tree that shows the atures…

A: The solution to the given question is:

Q: a) Which attribute would information gain choose as the root of the tree? b) Draw the decision tree…

A: ANSWER:-

Q: 4. Entropy in Decision Trees. entropy for each case below in a two class problem where the node has…

A:

Q: a) Consider the operation of the Minimax algorithm over the above search tree and indicate the value…

A: a) The value of each node will be : H = min(99,8) = 8 I = min(5,16) = 5 J = min(83,6) = 6 K =…

Q: The pruning threshold for the above decision tree is th=0.15 and when pruning is completed A4 What…

A: Value of A4 is 22.

Q: If a decision tree has 4 leaf-nodes, and each leaf-node represents a decision, namely either a Yes…

A:

Q: What values could you insert to cause a right-right imbalance, and at which node does the imbalance…

A: In the context of AVL trees, imbalances are conditions where the heights of subtrees violate the AVL…

Q: Discretized values in a decision tree may be combined into a single branch, if: Group of answer…

A: You can choose multiple answers here:order is not preserved: This is the primary condition for…

Q: Question 2: Consider the following set of training examples. a) Compute a two-level decision tree…

A: Decision Tree: The decision tree is the most powerful and popular tool for classification and…

Q: Bias toward selecting an attribute at a node of the decision tree may happen if the attribute has…

A: Explanation Bias toward selecting an attribute at a node of the decision tree may happen if the…

Q: a. Discuss what will happen you decide to change the splitting criterion. Explain how you are…

A: As per bartleby policy I have to answer only one question but still I answered for both. Hope this…

Q: ep-costs.  You also know the following heuristic estimates:  h(B->G2) = 9, h(D->G2)=10, h(A->G1)=2,…

A: A* search algorithm is an informed search algorithm that uses a combination of the cost of the path…

Q: Fill in the values for the empty nodes in the following game tree according to Minimax, where the…

A: Let's see the solution

Q: nlai nin how,

A: Pruning Pruning decreases the size of a Decision Tree which has the possibility to slightly…

Q: +ve class : p1(-2,2), p2(2,2) -ve class: p3(0,5) Find the decision boundary and margin also show…

A: Answer: I have given answered in the handwritten format in brief explained.

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: Fill in the values for the empty nodes in the following game tree according to Minimax, where the…

A: Given that, The tree represents a Minimax tree which is a game tree. In this tree, max player is…

Q: raw a game tree (upto depth level 2) for the given version of the Nim game. Please consider figure 1…

A: The Nim event's restrictions:   Nim is a computational game in which two participants receive turns…

Q: Explain the differences and similarities between decision trees and random forests in a few…

A: Machine learning is a subfield of artificial intelligence which is broadly defined as the capability…

Q: Consider a node, t, in a decision tree. The set of training records in node t is Dt, which contains…

A: Given data             Class                      Number of records              C1…

Q: Which XXX would replace the missing statement in the given algorithm for finding the uncle of a node…

A: An uncle is a person who is the brother of the father or mother in a family.  Therefore, the if…

Q: Create a decision tree based on the table, calculate the first tree separation in decision tree…

A: In composite manufacturing processes, ensuring the quality of the final products is paramount.…

Q: . How many decision trees are there with 3 binary attributes? With 4? . In class we looked at an…

A: 1.  Decision trees are there with 3 binary attributes:-------------For three attributes there are 7…

Q: Write a r programming code for decision tree use mtcars dataset and calculate these classification…

A: We have to build decision tree for mtcars dataset. We need to calculate these metric and plot the…

Q: A decision tree model built with MinimumCasesForSplit=20, MinCasesAtLeaf=10, Pre-pruning=Yes,…

A: MinCasesAtLeaf=5, Pre-pruning=Yes, Pruning=Yes and maxDepth=5

Q: Explain what random forests are and how they differ from decision trees in a single sentence or a…

A: Decision trees and random forestsrandom forest tree Decision trees are the only item that make up a…

Q: Can random forests be used to fix decision trees that tend to have high bias and low variance?

A: Introduction Decision tree: A model type utilized both for classification and regression is decision…

Q: You are given the following game tree. The top level is a maximizing level. A B D F 10 12 13 16 13…

A: Value of nodes:   D: 12 E: 16 F: 13 G: 17 B: 12 C: 13 A: 13

Q: Consider a classification by a decision tree problem. Consider a binary input variable being_student…

A: It is defined as a tool that has applications spanning several different areas. Decision trees can…

Q: Fold MaxDepth 1 2 3 1 0.9 0.8 0.5 2 0.5 0.8 0.6 What is the MaxDepth hyperparameter chosen by this…

A: max_depth = max number of levels

Q: what do you think are the other uses cases of decion tree. Justify your answer

A: А  deсisiоn  tree  is  а  suрроrt  tооl  with  а  tree-like  struсture  thаt  mоdels  рrоbаble…

Q: Consider the table below. If you apply the 1-Rule method teratively to the table to generate a…

A: - The given table shows the values for predicting the values for F3 with the help of a decision tree…

Q: Question 12 kana  .How would you conclude the generated decision tree is Overfitted or not? Explain…

A: Decision tree: Decision trees are a type of supervised machine-learning algorithm used for…

Q: What is the prediction of the decision tree…

A: we are tasked with utilizing a decision tree to make a prediction based on a set of predictor…

Q: Describe in as few as one or as many as a few words as possible the differences between a decision…

A: A decision tree is an algorithm used in machine learning to make decisions.

Q: • What is the entropy of the root node? Show your working. • Draw the complete decision tree. Are…

A: Decision Tree is a supervised learning technique. It is used for both classification and Regression.…

Q: the pessimistic error of this decision tree-

A: Given :- In the above given question a decision tree with the total number of training samples and…

Decision Tree

A decision tree is a unique kind of probability tree. A popular and powerful tool used for prediction and classification. The decision tree structure is the same as a flowchart with a tree structure. The internal node refers to the attribute test, the branch refers to the outcome of the test, and the leaf node(terminal) contains the class label.