regressioonülesande

A regressioonülesande, or regression task, is a fundamental problem in machine learning and statistics. The objective is to predict a continuous numerical output value based on a set of input features. Unlike classification tasks where the output is a discrete category, regression deals with quantities that can take on any value within a range.

For example, predicting the price of a house based on its size, number of bedrooms, and location is a classic regression problem. Other examples include forecasting stock prices, estimating a person's age based on their appearance, or predicting temperature based on weather data.

The process typically involves training a model on a dataset containing input features and their corresponding known output values. The model learns the relationship between these features and the target variable. Once trained, the model can be used to make predictions on new, unseen data. Common algorithms used for regression tasks include linear regression, polynomial regression, support vector regression, and decision trees. The performance of a regression model is evaluated using metrics like Mean Squared Error (MSE), Root Mean Squared Error (RMSE), or Mean Absolute Error (MAE), which quantify the difference between predicted and actual values.