Regressioonülesanne

Regressioonülesanne is the Estonian term for a regression task in the field of machine learning and statistics. It is a type of supervised learning problem where the goal is to predict a continuous output variable based on one or more input variables. Unlike classification tasks, which predict discrete categories, regression tasks aim to find a mapping function that outputs a numerical value.

The core idea behind a regressioonülesanne is to build a model that can approximate the relationship between the independent variables (features) and the dependent variable (target). This relationship is often represented by a mathematical function. Common examples of regression problems include predicting house prices based on features like size, location, and number of rooms, or forecasting stock prices based on historical data and market indicators.

Several algorithms can be employed to solve regressioonülesanne, including linear regression, polynomial regression, support vector regression (SVR), decision trees, and neural networks. The choice of algorithm depends on the complexity of the data and the desired accuracy. Model evaluation in regression typically involves metrics like Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and Mean Absolute Error (MAE), which quantify the difference between the predicted and actual values. The objective is to minimize these error metrics, indicating a better fit of the model to the data.