In artificial intelligence, a Regressor refers to a type of machine-learning model that specializes in solving regression problems. Regression is a predictive modeling technique where the goal is to predict a continuous numerical value as the output based on input features. A regressor is designed to learn and approximate the relationship between the input variables and the target variable, aiming to minimize the difference between its predicted values and the actual target values.
Regressors encompass a variety of algorithms and techniques, such as linear regression, polynomial regression, support vector regression, decision tree regression, and neural network regression. Each regressor has its strengths and weaknesses, making it suitable for different types of data and problem domains. For instance, linear regression assumes a linear relationship between variables, while decision tree regression captures non-linear relationships through a hierarchy of binary decisions. Regressors are essential tools in AI for making numerical predictions in fields like economics, finance, healthcare, and more. By learning from historical data and identifying patterns, regressors assist in making informed predictions about future outcomes.
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