Regression is a type of predictive modeling technique used to predict a continuous target variable (dependent variable) based on one or more predictor variables (independent variables).

In machine learning, regression models are used to understand relationships between variables, and they are often used in predicting numerical values like prices, quantities, etc. There are various types of regression techniques, including:

1. Linear Regression: Models the relationship between the dependent variable and independent variable(s) by fitting a linear equation.
2. Polynomial Regression: A type of linear regression that fits a polynomial equation to the data, allowing for more complex relationships.
3. Ridge Regression: A type of linear regression that includes a regularization term to avoid overfitting.
4. Lasso Regression: Another regularization technique, Lasso regression adds a penalty term that encourages some coefficients to be exactly zero, leading to sparser models.
5. Support Vector Regression: Utilizes support vector machines to model a nonlinear relationship between the dependent and independent variables.
6. Decision Tree Regression: Utilizes decision trees to segment the predictor space into simple regions, allowing for both linear and nonlinear relationships.
7. Random Forest Regression: An ensemble of decision trees that helps in reducing overfitting and provides more robust predictions.
8. Neural Network Regression: Using artificial neural networks to model complex relationships between input and output. This can be highly flexible and can model both linear and nonlinear patterns.

When working with regression models, careful preprocessing of data, feature engineering, model selection, and hyperparameter tuning are crucial to achieving accurate predictions.