I am completely lost for this practice for Machine Learning. There was no solution provided for this.Train a decision tree to predict whether a person makes over $50K a year based on the UCI Adult Census Income dataset. This database contains 48842 instances. Each instance is described in terms of 14 attributes. The last column attribute named “income” is the binary outcome to predict (i.e., ≤ 50K or otherwise). Note there are both numeric and discrete attributes.Cannot allowed to use libraries to such as SciKit to help. Code must be in Python.Tasks:The data set can be downloaded from the website https://www.kaggle.com/ uciml/adult-census-income. After downloading the data set, please split it into training set (70%), validation set (10%) and test set (20%).Implement the C4.5 decision tree algorithm. Your implementation should let user determine when to stop the recursive splitting.Run your algorithm on the training set to learn a decision tree using the following cut-off values, i.e. θI = 0.2, 0.4, 0.6, 0.8 in the attached pseudocode and also covered in class. Plot the training error for each cut-off? Hint: you can use training set to grow the tree and use validation set to calculate training error.Test your decision trees on the test set. And plot test errors together with training errors for each tree you train. What is your conclusion? Generate Tree(X)If NodeEntropy(X)< 01 /* eq. 9.3Create leaf labelled by majority class in XReturni - SplitAttribute(X)For each branch of æ;Find X; falling in branchGenerate Tree(Xi)SplitAttribute(X)MinEnt- MAXFor all attributes i = 1, ..., d%3DIf æ; is discrete with n valuesSplit X into X1, . . . , Xn by æ¿e - SplitEntropy(X1, ..., Xn) /* eq. 9.8 */If e

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Computer Networking: A Top-Down Approach (7th Edition)

7th Edition

ISBN:9780133594140

Author:James Kurose, Keith Ross

Publisher:James Kurose, Keith Ross

Chapter1: Computer Networks And The Internet

Section: Chapter Questions

Problem R1RQ: What is the difference between a host and an end system? List several different types of end...

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I am completely lost for this practice for Machine Learning. There was no solution provided for this.

Train a decision tree to predict whether a person makes over $50K a year based on the UCI Adult Census Income dataset. This database contains 48842 instances. Each instance is described in terms of 14 attributes. The last column attribute named “income” is the binary outcome to predict (i.e., ≤ 50K or otherwise). Note there are both numeric and discrete attributes.

Cannot allowed to use libraries to such as SciKit to help. Code must be in Python.

Tasks:

The data set can be downloaded from the website https://www.kaggle.com/ uciml/adult-census-income. After downloading the data set, please split it into training set (70%), validation set (10%) and test set (20%).
Implement the C4.5 decision tree algorithm. Your implementation should let user determine when to stop the recursive splitting.
Run your algorithm on the training set to learn a decision tree using the following cut-off values, i.e. θI = 0.2, 0.4, 0.6, 0.8 in the attached pseudocode and also covered in class. Plot the training error for each cut-off? Hint: you can use training set to grow the tree and use validation set to calculate training error.
Test your decision trees on the test set. And plot test errors together with training errors for each tree you train. What is your conclusion?