In the code cell below, build and train two random forest models, one with $20$ estimators and one with $100$ estimators.

Use RandomForestClassifier$()$ to create a model object, and assign the result to the variable rf$\_20\_$model. You will provide the following arguments: criterion$=$'entropy', n$\_$estimators$=20.$

Fit rf$\_20\_$model to the training data.

Use the predict$\_$proba$()$ method to use the fitted model rf$\_20\_$model to predict values for the test data. The method will return two columns. Store the values of the second column to a list named rf$\_20\_$predictions $($Note: Remember to convert the resulting array to a list$).$

Use RandomForestClassifier$()$ to create a model object, and assign the result to the variable rf$\_100\_$model. You will provide the following arguments: criterion$=$'entropy', n$\_$estimators$=100.$

Fit rf$\_100\_$model to the training data.

Use the predict$\_$proba$()$ method to use the fitted model rf$\_100\_$model to predict values for the test data. The method will return two columns. Store the values of the second column to a list named rf$\_100\_$predictions $($Note: Remember to convert the resulting array to a list$).$