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In Data Science, Decision Tree classifiers are widely used for both classification and regression tasks because of their simplicity, interpretability, and ability to handle both numerical and categorical data. A Decision Tree splits the dataset into subsets based on the value of input features, creating a tree-like structure where each internal node represents a feature, each branch represents a decision rule, and each leaf node represents an outcome or class label. In R, creating a Decision Tree classifier involves loading a dataset, building the model using a suitable library such as `rpart`, and visualizing the results for interpretation. For a sample dataset, the built-in `iris` dataset can be used, which contains 150 instances of iris flowers with ________ __________ _________ _________ __________ ____ ___ ______ _____ _____ _______ _____.
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