How Decision Trees Work?
The process of creating a decision tree involves:
- Selecting the Best Attribute: Using a metric like Gini impurity, entropy, or information gain, the best attribute to split the data is selected.
- Splitting the Dataset: The dataset is split into subsets based on the selected attribute.
- Repeating the Process: The process is repeated recursively for each subset, creating a new internal node or leaf node until a stopping criterion is met (e.g., all instances in a node belong to the same class or a predefined depth is reached).
Decision Tree
Decision trees are a popular and powerful tool used in various fields such as machine learning, data mining, and statistics. They provide a clear and intuitive way to make decisions based on data by modeling the relationships between different variables. This article is all about what decision trees are, how they work, their advantages and disadvantages, and their applications.