Train an ID3 decision tree for a dataset shown in the following table. The table contains 2 categorical attributes (refund and marital status) and 1 continuous attribute (taxable income). Once you got the model then use it to classify the input X1 (No, Single, 95K) and X2 (Yes, Divorced, 120K)

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: Consider the dataset in terms of attribute-value pairs where F is the value, and A.B, C are…

A: Solution ::  Q: What is the entropy of the dataset?   Ans:   We can think about the entropy of a…

Q: put attributes. We learn a decision tree classifier. Wind Play Tennis No Temp. Humidity High High…

A: It is defined as a file that contains one or more records. The record is the basic unit of…

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Q: In Priscilla Moraes’s work, briefly describe her methodology for finding synonym words for “graph…

A: Answer is given below-

Q: Attribute selection approaches in decision trees determine whether a nominal attribute is excellent…

A: Introduction TECHNIQUES FOR ATTRIBUTE SELECTION: The term "attribute selection technique" refers to…

Q: Create a decision tree from the data below where the final classes are a binary response to the…

A: Note: Answering the question in python as a programing language is mentioned. Task : Given the…

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Q: Q.6 Explain impurity measures in decision tree.

A:

Q: 4. You are given the count matrix in the table below for the attribute "Previous GPA". The class…

A: Data mining refers to techniques for extracting hidden, interesting patterns and knowledge fromlarge…

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A: The solution for the above given question is given below:

Q: Computing as a discipline Difference between Regression Trees and Classification Trees When using…

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Q: For the following dataset, which is the best feature to condition on for a decision tree? Feature-1…

A: Explanation: Feature 2 as the count of 2's are maximum among all features and hence it can be picked…

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A: Answer:-

Q: nction plotDecisionBoundary (theta, X, y) LOTDECISIONBOUNDARY Plots the data points X and y into a…

A: function plotDecisionBoundary(theta, X, y) % Plots X and y into a new figure with the decision…

Q: The field of computer science What is the difference between Regression Trees and Classification…

A: Please find the detailed answer in the following steps.

Q: Consider the dataset in terms of attribute-value pairs where F is the value, and A,B, C are…

A: We can think about the entropy of a dataset in terms of the probability distribution of observations…

Q: Kalembe has a machine learning problem that has real-valued attributes in a 100-dimensional space.…

A: I Have answered this question in step 2.

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: Data is often segmented using decision trees, with each branch splitting the data set in half or…

A: The answer is given in the below step

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

A: The Answer is

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Q: PART 2:  Using following columns build a model to predict the ff person would survive or not, •…

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A:

Q: In decision trees, attribute selection techniques decide the goodness of a nominal attribute based…

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A: (a) D-i-D regression with treatment indicator:

Q: What feature will be at the top for a decision tree?

A: ANSWER: Decision Tree: Decision tree is the most impressive and well known instrument for…

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: XOR function mappings can easily be classified by decision trees. True False

A: The answer is

Q: At each branch, the data is partitioned into two or more categories using a decision tree. If the…

A: Decision trees are a popular machine learning technique used to solve classification and regression…

Q: Select all that is true. G has 6 edges G has 6 vertices G is an undirected graph OG contains loops

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Q: You are using a decision tree classifier and one of the features is the GPA of the student. If there…

A: The question wants to know the distinct GPA values if the number of possible splits are 511.

Q: The field of computer science What is the difference between Regression Trees and Classification…

A: You've asked many questions. Let's go over each question one by one. What's the difference between…

Q: Train a fine tree model ( minimum leaf size of 4) to predict AveSpeed using predictor variables…

A: RMSE The root-mean-square deviation (RMSD) or root-mean-square error (RMSE) is a frequently used…

Q: Task 1: For the training set given below, predict the classification of the following sample X =…

A: Simple Naive bayes classifier I will consider to classify the given data.   Naive bayes classifier :…

Q: A person is given a machine learning problem that has real-valued attributes in a 100-dimensional…

A: I Have answered this question in step 2.

Q: Name Smoking Weight hypertension A Yes Overweight Yes B Yes Underweight No C No Underweight No D…

A: Below is the complete solution with explanation in detail for the given question about Decision Tree…

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…

Temporal difference learning

Temporal difference learning (TD learning) is a concept in machine learning that attributes to a class of model-free reinforcement learning methods. The technique predicts a reward or quantity depending on the future values of the value function.