Now, let$$s use scikit$-$learn to train a DecisionTreeClassifier and KNeighborsClassifier on

the data.

Using the predictive capabilities of the scikit$-$learn package is very simple. In fact, it can be

carried out in three simple steps: initializing the model, fitting it to the training data, and predicting

new values$\mathrm{.}1$

$($b$)(0$ pts$)$ Before trying out any classifier, it is often useful to establish a baseline. We have

implemented one simple baseline classifier, MajorityVoteClassifier, that always predicts

the majority class from the training set. Read through the MajorityVoteClassifier and

its usage and make sure you understand how it works.

Your goal is to implement and evaluate another baseline classifier, RandomClassifier, that

predicts a target class according to the distribution of classes in the training data set. For

example, if $85\%$ of the examples in the training set have $>50$k $=0$ and $15\%$ have $>50$k $=1,$

then, when applied to a test set, RandomClassifier should randomly predict $85\%$ of the

examples as $>50$k $=0$ and $15\%$ as $>50$k $=1.$

Implement the missing portions of RandomClassifier according to the provided specifications.

Then train your RandomClassifier on the entire training data set, and evaluate its

training error. If you implemented everything correctly, you should have an error of $0\mathrm{.}374$

or $0\mathrm{.}385.$