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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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(Classification with the Iris Dataset) We used unsupervised learning with the Iris dataset to cluster its samples. This dataset is in fact labeled so it can be used with scikit learn's supervised machine learning estimators. Use the techniques you learned in the Digits dataset classification case study to load the Iris dataset and perform classification on it with the k-nearest neighbors algorithm. Use a KNeighborsClassifier with the default k value. • What is the prediction accuracy? • Using scikit-learn's KFold class and cross_val_score function, determine the optimal k value for classifying Iris samples using a KNeighborsClassifier. • As we did in the digits case study, run multiple classification estimators for the Iris dataset and compare the results to see which one performs best.
Transcribed Image Text:(Classification with the Iris Dataset) We used unsupervised learning with the Iris dataset to cluster its samples. This dataset is in fact labeled so it can be used with scikit learn's supervised machine learning estimators. Use the techniques you learned in the Digits dataset classification case study to load the Iris dataset and perform classification on it with the k-nearest neighbors algorithm. Use a KNeighborsClassifier with the default k value. • What is the prediction accuracy? • Using scikit-learn's KFold class and cross_val_score function, determine the optimal k value for classifying Iris samples using a KNeighborsClassifier. • As we did in the digits case study, run multiple classification estimators for the Iris dataset and compare the results to see which one performs best.
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