In supervised machine learning, you work with data to train models that can make predictions or decisions. To do this effectively, you need to understand three essential terms: features, labels, and datasets. Features are the input variables you use to make predictions. They describe the characteristics or properties of your data points. For example, in a dataset of houses, features might include the number of bedrooms, square footage, and location. Labels, on the other hand, are the outputs or target values you want the model to predict. Continuing the house example, the label could be the price of each house. The dataset is the collection of all your data points, with each point consisting of both features and a label. This structure allows you to teach a model the relationship between inputs and outputs.

When you train a supervised learning model, you provide it with both features and labels from your dataset. The model learns to associate patterns in the features with the correct labels. Later, you can evaluate the model by checking how well it predicts labels for new data points based only on their features. This process of using features and labels together is at the heart of supervised learning, enabling you to build models that solve real-world prediction problems.