Regressiotehtävät

Regressiotehtävät, often translated as regression tasks or regression problems, are a fundamental concept in machine learning and statistics. The primary goal of a regression task is to predict a continuous numerical output value based on one or more input features. Unlike classification tasks, which aim to assign an input to a discrete category, regression seeks to estimate a quantity.

Common examples of regression tasks include predicting house prices based on features like square footage, number of bedrooms, and location; forecasting stock market prices; estimating a person's age based on their appearance; or predicting the temperature for the next day.

The process typically involves training a model on a dataset of historical data, where both the input features and the corresponding known output values are available. The model learns the relationship between these features and the target variable. Once trained, the model can then be used to make predictions on new, unseen data. Various algorithms are employed for regression, such as linear regression, polynomial regression, support vector regression, and decision tree regression, each with its own strengths and assumptions. The performance of a regression model is usually evaluated using metrics like Mean Squared Error (MSE), Root Mean Squared Error (RMSE), or Mean Absolute Error (MAE), which quantify the difference between the predicted and actual values.