# 代写DS 310课程作业、代做Machine Learning作业、python实验作业代做、python编程设计作业调试代做R语言编程|帮做R语言编程

Lab Assignment 2
Vasant Honavar
DS 310 - Machine Learning
Available: Nov 4, 2019
Nov 11, 2019
In all of the following exercises, if there is a need for a random seed, set it to 1234. Using
different sklearn libraries are permitted as long as usage is well-understood and explained in
the code. In case you would need to interpret your results, do so in your Ipython Notebook
by changing the cell type and writing your interpretation immediately below the code and
its result so that the interpretation can be matched with the result and the code. Submit a
single Ipython Notebook in which all of the answers are organized in a way that can be run
and evaluated.
1. Random forest (RF). Import the Breast Cancer data set from sklearn. Train and
evaluate using 5-fold cross-validation, a Random Forest Classifier from the ensemble
library of sklearn using 100 trees. Report the following:
(a) The average Accuracy, Sensitivity, Specificity, and AUC across all 5 runs of the
cross-validation.
(b) Report the average feature importance score for each feature across all 5 runs of
the cross-validation.
2. Multinomial Naive Bayes Classifier. Import the 20 News Groups data set from
sklearn. Preprocess the news articles to obtain the bag of words representation of the
data using the sklearn library functions. See sklearn tutorials on text analytics for
documentation. Train and evaluate a Multinomial Naive Bayes classifier from sklearn
using 5-fold cross-validation.
Report the average Accuracy, Sensitivity, Specificity, and AUC across all 5 runs of the
cross-validation.

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