In supervised learning, there are two main ways to analyze data: parametric and non-parametric methods. The biggest difference between them is how they make guesses about the data.

Parametric methods:

• These methods assume the data fits a certain shape. For example, they might follow a simple line equation like $y = mx + b$ in linear regression.
• They are usually quicker to calculate.
• They need less data to figure out important numbers or parameters.

Non-parametric methods:

• These methods do not assume anything about how the data is organized. They include techniques like k-NN (k-Nearest Neighbors) or decision trees.
• They are more flexible, meaning they can handle many different types of patterns in the data.
• However, they typically need more data to work well.

It's important to choose the right method based on the type of data you have!