Regressiotehtävä

Regressiotehtävä is a Finnish term that translates to "regression task" or "regression problem" in English. It refers to a type of supervised machine learning problem where the goal is to predict a continuous numerical value. Unlike classification tasks, which aim to categorize data into discrete classes, regression problems predict a quantity.

In a regressiotehtävä, the algorithm learns a mapping from input features to a target variable that can take any real value within a given range. Common examples include predicting house prices based on features like size and location, forecasting stock market trends, estimating the temperature based on atmospheric data, or determining the lifespan of a product.

The process typically involves training a model on a dataset of labeled examples, where each example consists of input features and the corresponding known output value. The model then uses this learned relationship to make predictions on new, unseen data. Various algorithms can be employed for regressiotehtävä, including linear regression, polynomial regression, support vector regression, and decision tree regression, among others. The choice of algorithm depends on the complexity of the data and the desired accuracy of the predictions. Evaluating the performance of a regression model usually involves metrics such as Mean Squared Error (MSE), Root Mean Squared Error (RMSE), or Mean Absolute Error (MAE).