Discretization of continuous features

Imagine you have a ruler that measures things in inches. This ruler is very precise and can measure things to the nearest 0.1 inch. But sometimes, you don’t need that much precision. For example, let’s say you want to measure the height of a group of people who are all standing next to each other. It would be really hard to use the ruler to measure every single person’s height to the nearest tenth of an inch. So instead, you might decide to simplify things by rounding the measurements to the nearest inch.

That's exactly what happens when we discretize continuous features in data analysis. Continuous features are variables that can have any value within a certain range, like temperature or age. But sometimes, we don't need that much detail. So we "round" or "bin" the values into groups. Instead of saying someone is 5.7 feet tall, we might group them with everyone who's between 5.5 and 6 feet tall.

This makes it easier to analyze the data and to see patterns. It can also help reduce the risk of overfitting, which is when a model fits too closely to the training data and doesn't generalize well to new data. By discretizing the continuous features, we can simplify the model and avoid overfitting.