$10$ iris$-$bagging datasets $($for training $10$ classifiers$),$ each with $30$ records

$1$ test dataset with $150$ records

$$ Attributes: sepal length, sepal width, petal length, petal width

$$ Training and evaluating classifiers

$$ Generate a decision tree classifier model using the sklearn

DecisionTreeClassifier function

$$ Run $10($training$)$ bagging rounds.

$$ Using the test dataset, predict the labels using majority voting.

$$ Calculate the accuracy using confusion matrix.

In python!