1.simplelinearregression: This is the simplest linear regression model with only one input element and one output variable. It tries to find a linear equation that best fits the data.
2. Multiplelineargression: This is a more complex linear regression model with multiple input elements and one output variable. It tries to find a multivariate linear equation that is most suitable for data.
3.RidgeRegression: This is a model that regularizes multiple linear regression to prevent over-fitting. This is achieved by adding L2 regularization term to the loss function.
4.Lasso regression: This is a regularized multiple linear regression model, which can not only prevent over-fitting, but also realize feature selection. It is realized by adding the regular term L 1 to the loss function.
5.RidgeRegression: This is a model that regularizes multiple linear regression to prevent over-fitting. This is achieved by adding L2 regularization term to the loss function.
6. Elastic regression: This model is a combination of Lasso and Ridge, which can not only prevent over-fitting, but also realize feature selection.
7.LogisticRegression: Although there is the word "regression" in the name, logistic regression is actually a classification algorithm, not a regression algorithm. It tries to find a function that can map the input elements to a specific probability value, which can be interpreted as the probability of an event.