モデル regression is a fundamental statistical method used in various fields, including economics, biology, and 人工知能, to establish the relationship between variables. At its core, 回帰分析 seeks to predict the value of a dependent variable (often referred to as the target) based on the values of one or more independent variables (also known as predictors or features).
いくつかのタイプの回帰モデルがあり、最も一般的なのは 線形回帰, where the relationship between the dependent and independent variables is assumed to be linear. In this case, the model is represented by a straight line, described by the equation y = mx + b, where y is the dependent variable, x is the independent variable, m is the slope, and b は y切片です。
より複雑な回帰の形態には 多重回帰, which involves multiple independent variables, and 非線形回帰, which can model relationships that are not linear. Other specialized regression techniques, such as リッジ回帰 and LASSO回帰, are used to prevent overfitting by introducing penalties for including too many variables.
回帰分析は広く 機械学習で使用される to build predictive models. The models are trained on historical data, allowing them to learn patterns and make predictions about future or unseen data. Evaluation metrics, such as Mean Squared Error (MSE), are commonly used to assess the performance of regression models, providing insights into their predictive accuracy.