What is the detailed working mechanism of Decision Trees, and how are they utilized for classification tasks?

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Decision Trees are a popular machine learning algorithm used for classification tasks by recursively splitting the data based on feature values.

Decision Trees work by recursively splitting the data based on feature values to create a tree-like structure.
At each node, the algorithm selects the feature that best splits the data into purest possible subsets.
This process continues until a stopping criterion is met, such as a maximum tree depth or minimum number of samples in a node.
Decision Trees are utilized for classification tasks by traversing the tree to make predictions based on the feature values of new instances.
For example, in a medical diagnosis task, a Decision Tree can be trained on patient data to predict whether a patient has a certain disease based on symptoms.

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