Decision Tree

A decision tree is a graphical representation of a decision-making process that uses a tree-like model of decisions and their possible consequences. It consists of nodes that represent decisions or actions, branches that represent possible outcomes, and leaves that represent final decisions or outcomes. Decision trees are commonly used in machine learning and data analysis for both classification and regression tasks. They can be used to identify important features or variables and to understand complex decision-making processes. The construction of a decision tree involves recursively splitting the data into subsets based on the values of input features until a stopping criterion is met, such as a maximum depth or minimum number of samples per leaf. The resulting tree can be used to make predictions on new input data by following the path from the root node to the corresponding leaf node.

This code creates an instance of the DecisionTreeClassifier class and fits it to the Iris dataset. The plot_tree function is then used to create a plot of the decision tree. The filled and rounded parameters are set to True to create a more visually appealing plot, and the feature_names and class_names parameters are set to the corresponding names from the dataset for improved readability. Finally, the show method is used to display the plot.