👉 Regression computing is a subset of machine learning focused on predicting continuous outcomes or values. It involves building models that establish a relationship between input features and a target variable, which is expected to be a number on a continuous scale, such as predicting house prices, temperature, or sales figures. The model learns from historical data by identifying patterns and correlations between the input features and the target variable, then uses this understanding to forecast new, unseen data. Common techniques include linear regression, polynomial regression, decision trees, and neural networks, each suited to different types of data and relationships. The goal is to minimize prediction errors, ensuring the model's predictions are as accurate as possible for future data points.
