What is Supervised Learning?
Supervised learning is a common machine learning approach where an algorithm is trained using a "labeled" dataset. This means that every piece of data provided to the AI includes the correct answer. The algorithm's job is to figure out the mathematical relationship between the input data and the provided answer.
How does it work?
During training, the model makes a prediction on the input data. Since the dataset contains the correct answer (the label), the system immediately compares its prediction to the truth. If it is wrong, it calculates the error and adjusts its internal parameters. It repeats this process until its predictions consistently match the provided labels.
What is a simple example?
If you want to train a model to predict house prices, you would provide a dataset where every row contains the square footage, number of bedrooms, and location of a house (the inputs), as well as the exact price the house sold for (the label). The model learns the correlations so that when you feed it a new house without a price, it can accurately predict the value.
Why does it matter?
Supervised learning is highly accurate and is the industry standard for classification tasks (like spam detection) and regression tasks (like forecasting revenue). However, it is expensive and time-consuming because humans must manually label the massive datasets required.