Based on the histograms, which attribute appears to be the most useful for classifying wine, and why?

What is the accuracy $-$ the percentage of correctly classified instances $-$ achieved by ZeroR when you run it on the training set? Why is ZeroR a helpful baseline for interpreting the performance of other classifiers?

Using a decision tree Weka learned over the training set, what is the most informative single feature for this task, and what is its influence on wine quality? Does this match your answer from question $1?$

What is $10-$fold cross$-$validation? What is the main reason for the difference between the percentage of Correctly Classified Instances when you measured accuracy on the training set itself, versus when you ran $10-$fold cross$-$validation over the training set? Why is cross$-$validation important?

What is the "command$-$line" for the model you are submitting? For example, $"$J$48-$C $0\mathrm{.}25-$M $2".$ What is the reported accuracy for your model using $10-$fold cross$-$validation?

In a few sentences, describe how you chose the model you are submitting. Be sure to mention your validation strategy and whether you tried varying any of the model parameters.

A Wired magazine article from several years ago on the 'Peta Age' suggests that increasingly huge data sets, coupled with machine learning techniques, makes model building obsolete. In particular it says: This is a world where massive amounts of data and applied mathematics replace every other tool that might be brought to bear. Out with every theory of human behavior, from linguistics to sociology. Forget taxonomy, ontology, and psychology... In a short paragraph $($about four sentences$),$ state whether you agree with this statement, and why or why not.

Briefly explain what strategy you used to obtain the Classifiers A and B that performed well on one of the car or wine data sets, and not the other.

Name one major difference between the output space for the car data set vs$.$ the wine data set, that might make some classifiers that are applicable to the wine data not applicable to the car data.