Explain working of decision trees, how to select parent and child nodes, gini impurity, etc?

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Decision trees are a popular machine learning algorithm used for classification and regression tasks.

Decision trees are a flowchart-like structure where each internal node represents a feature or attribute, each branch represents a decision rule, and each leaf node represents the outcome.
To select parent and child nodes, the algorithm calculates the best split at each node based on criteria like Gini impurity or information gain.
Gini impurity is a measure of how often a randomly chosen element would be incorrectly classified. The goal is to minimize the impurity in each node.
The algorithm recursively splits the data based on the selected feature until a stopping criterion is met, such as reaching a maximum depth or minimum number of samples per leaf.
Example: In a decision tree for predicting whether a customer will buy a product, the parent node might be 'income' and the child nodes could be 'high income' and 'low income'.

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