Lab Assignment 2Vasant HonavarDS 310 - Machine LearningAvailable: Nov 4, 2019Nov 11, 2019In all of the following exercises, if there is a need for a random seed, set it to 1234. Usingdifferent sklearn libraries are permitted as long as usage is well-understood and explained inthe code. In case you would need to interpret your results, do so in your Ipython Notebookby changing the cell type and writing your interpretation immediately below the code andits result so that the interpretation can be matched with the result and the code. Submit asingle Ipython Notebook in which all of the answers are organized in a way that can be runand evaluated.1. Random forest (RF). Import the Breast Cancer data set from sklearn. Train andevaluate using 5-fold cross-validation, a Rand代写DS 310、代做Machine Learning、pyom Forest Classifier from the ensemblelibrary of sklearn using 100 trees. Report the following:(a) The average Accuracy, Sensitivity, Specificity, and AUC across all 5 runs of thecross-validation.(b) Report the average feature importance score for each feature across all 5 runs ofthe cross-validation.2. Multinomial Naive Bayes Classifier. Import the 20 News Groups data set fromsklearn. Preprocess the news articles to obtain the bag of words representation of thedata using the sklearn library functions. See sklearn tutorials on text analytics fordocumentation. Train and evaluate a Multinomial Naive Bayes classifier from sklearnusing 5-fold cross-validation.Report the average Accuracy, Sensitivity, Specificity, and AUC across all 5 runs of thecross-validation.1转自：http://www.3daixie.com/contents/11/3444.html
讲解：DS 310、Machine Learning、python、pythonR|R
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