# Regression Types

Regression is a type of supervised machine learning task that is used to predict continuous values (i.e., real numbers) for a given input data. There are several types of regression algorithms, including:

Simple linear regression: This is a basic regression model that assumes a linear relationship between the independent and dependent variables. It is used to predict the value of a continuous target variable based on the value of a single independent variable.

Logistic regression: This type of regression models the relationship between the independent variables and a binary dependent variable. The goal is to predict the probability of the target variable being one of two possible outcomes.

Decision tree regression: This type of regression builds a tree-like model that uses a series of binary decisions to predict the target variable based on the values of the independent variables.

Random forest regression: This type of regression builds an ensemble of decision trees and averages their predictions to make the final prediction. It is more robust to overfitting and noisy data compared to decision tree regression.

Neural network regression: Neural networks can adjust weights and biases of a model during training to minimize the difference between the predicted values and the actual values. One advantage of using neural networks for regression is that they are able to model complex, non-linear relationships between the variables.

Simple linear regression: This is a basic regression model that assumes a linear relationship between the independent and dependent variables. It is used to predict the value of a continuous target variable based on the value of a single independent variable.

Logistic regression: This type of regression models the relationship between the independent variables and a binary dependent variable. The goal is to predict the probability of the target variable being one of two possible outcomes.

Decision tree regression: This type of regression builds a tree-like model that uses a series of binary decisions to predict the target variable based on the values of the independent variables.

Random forest regression: This type of regression builds an ensemble of decision trees and averages their predictions to make the final prediction. It is more robust to overfitting and noisy data compared to decision tree regression.

Neural network regression: Neural networks can adjust weights and biases of a model during training to minimize the difference between the predicted values and the actual values. One advantage of using neural networks for regression is that they are able to model complex, non-linear relationships between the variables.

Updated on: 30/01/2023

Thank you!