What are Decision Trees?

Decision trees are flowcharts that depict decision-making processes by outlining various courses of action and their potential outcomes. They are utilized in diverse fields such as finance, healthcare, marketing, and computer science. Decision trees serve as a type of supervised machine learning algorithm that employs true or false responses to specific questions for data classification or regression. The resulting structure is a tree comprising different types of nodes, including root, internal, and leaf nodes.

How do Decision Trees work in machine learning?

In machine learning, decision trees function by recursively splitting the dataset into subsets based on the most significant attribute at each node. This process continues until a stopping criterion is met, resulting in the creation of a tree structure that can be used for classification or regression tasks.

Decision trees are advantageous due to their interpretability and ability to handle both numerical and categorical data effectively. They are particularly useful for understanding the decision-making process and identifying important features within the data.

What are the limitations of using Decision Trees in complex datasets?

When dealing with complex datasets, decision trees may face limitations such as overfitting, especially when the tree is too deep or when there is noise in the data. Additionally, decision trees may struggle with capturing relationships between variables that are not linear or require more sophisticated modeling techniques. In such cases, other machine learning algorithms like random forests or gradient boosting may be more suitable for handling the complexity of the data.