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2. Consider a binary-class classification problem, where there are equal numbers of positive and negative examples in the data set. Also suppose that the class label (i.e. +/- is totally randomly assigned to these examples. Now for the following two classification models. estimate their test accuracies based on different estimation methods. (i) A perfect decision tree model trained from the training data without pruning. (a) Repeated random subsampling method, in which 2/3 of data for training, the rest for testing. Test Accuracy = ? (b) Stratified 10-fold CV. Test Accuracy = ? (c) LOOCV. Test Accuracy = ? (ii) A majority predictor model trained from the training data that always predicts the majority class in training data. (a) Repeated random subsampling method, in which 2/3 of data for training, the rest for testing. Test Accuracy = ? (b) Stratified 10-fold CV. Test Accuracy = ? (c) LOOCV. Test Accuracy = ?

2. Consider a binary-class classification problem, where there are equal numbers of positive and negative examples in the data set. Also suppose that the class label (i.e. +/- is totally randomly assigned to these examples. Now for the following two classification models. estimate their test accuracies based on different estimation methods. (i) A perfect decision tree model trained from the training data without pruning. (a) Repeated random subsampling method, in which 2/3 of data for training, the rest for testing. Test Accuracy = ? (b) Stratified 10-fold CV. Test Accuracy = ? (c) LOOCV. Test Accuracy = ? (ii) A majority predictor model trained from the training data that always predicts the majority class in training data. (a) Repeated random subsampling method, in which 2/3 of data for training, the rest for testing. Test Accuracy = ? (b) Stratified 10-fold CV. Test Accuracy = ? (c) LOOCV. Test Accuracy = ?

Computer Networking: A Top-Down Approach (7th Edition)
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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2. Consider a binary-class classification problem, where there are equal numbers of positive and negative examples in the data set. Also suppose that the class label (i.e. +/-) is totally randomly assigned to these examples. Now for the following two classification models, estimate their test accuracies based on different estimation methods. (i) A perfect decision tree model trained from the training data without pruning. (a) Repeated random subsampling method, in which 2/3 of data for training, the rest for testing. Test Accuracy = ? (b) Stratified 10-fold CV. Test Accuracy =? (c) LOOCV. Test Accuracy = ? (ii) A majority predictor model trained from the training data that always predicts the majority class in training data. (a) Repeated random subsampling method, in which 2/3 of data for training, the rest for testing. Test Accuracy = ? (b) Stratified 10-fold CV. Test Accuracy = ? (c) LOOCV. Test Accuracy = ?
Transcribed Image Text:2. Consider a binary-class classification problem, where there are equal numbers of positive and negative examples in the data set. Also suppose that the class label (i.e. +/-) is totally randomly assigned to these examples. Now for the following two classification models, estimate their test accuracies based on different estimation methods. (i) A perfect decision tree model trained from the training data without pruning. (a) Repeated random subsampling method, in which 2/3 of data for training, the rest for testing. Test Accuracy = ? (b) Stratified 10-fold CV. Test Accuracy =? (c) LOOCV. Test Accuracy = ? (ii) A majority predictor model trained from the training data that always predicts the majority class in training data. (a) Repeated random subsampling method, in which 2/3 of data for training, the rest for testing. Test Accuracy = ? (b) Stratified 10-fold CV. Test Accuracy = ? (c) LOOCV. Test Accuracy = ?
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