Decision Trees and Random Forests are both popular machine learning algorithms used for classification and regression tasks, but they work in slightly different ways. Let’s break down how each of them operates:

Decision Trees:

Imagine a decision tree as a flowchart where each internal node represents a “test” or “question” on an attribute (e.g., Is the weather sunny?), each branch represents the outcome of the test (Yes or No), and each leaf node represents a class label (decision taken after computing all attributes). The paths from root to leaf represent classification rules.

In essence, a decision tree takes an input object (like the details of a day’s weather), and based on the attributes of the object (like temperature, humidity, etc.), it follows the decisions in the tree until it reaches a leaf node, which gives the output classification (like whether to play outside or not).

Decision trees are straightforward to understand and interpret, making them a popular choice for many. However, they can become very complex with many layers, which can lead to overfitting the training data and not generalizing well to new data.

Random Forests:

A Random Forest is essentially a collection (or “forest”) of Decision Trees, typically trained with the “bagging” method. The basic idea is to create multiple Decision Trees from randomly selected subsets of the training set (both samples and features) and then to combine their predictions. When making a prediction, each tree in the forest votes, and the most popular class label (for classification tasks) or the average prediction (for regression tasks) becomes the model’s output.

Random Forests address some of the main limitations of a single Decision Tree, particularly overfitting, by averaging multiple trees’ predictions to improve accuracy and robustness. They can handle large datasets with high dimensionality and provide estimates of feature importance.