In this task of supervised machine learning, The objective of the task is to create and train a decision tree using the Iris dataset. Afterwards we also have to make predictions on the new dataset and test the accuracy of the model.
Decision Tree Algorithm
Decision trees are a type of machine-learning algorithm that can be used for both classification and regression tasks. They work by learning simple decision rules inferred from the data features. These rules can then be used to predict the value of the target variable for new data samples. Decision trees are represented as tree structures, where each internal node represents a feature, each branch represents a decision rule, and each leaf node represents a prediction. The algorithm works by recursively splitting the data into smaller and smaller subsets based on the feature values. At each node, the algorithm chooses the feature that best splits the data into groups with different target values.