📄️ Multiple Linear Regression
Linear regression is a statistical modeling technique used to understand and analyze the relationship between a dependent variable and one or more independent variables. It assumes a linear relationship between the variables, meaning that the dependent variable can be expressed as a linear combination of the independent variables.
📄️ Artificial Neural Network Regression
Artificial Neural Network (ANN) regression is a technique used in machine learning to solve regression problems. Regression refers to the task of predicting a continuous numerical value, such as predicting house prices, stock prices, or the age of a person based on certain input variables or features.
📄️ Gradient Boosting Regression
Gradient Boosting regression is a machine learning technique used for solving regression problems. It is an ensemble method that combines multiple weak prediction models, typically decision trees, to create a strong predictive model. The key idea behind gradient boosting regression is to iteratively train new models that focus on the errors made by the previous models, gradually improving the overall prediction accuracy.
📄️ Random Forest Regression
Random Forest regression is a machine learning technique that utilizes an ensemble of decision trees to perform regression tasks. It is an extension of the Random Forest algorithm, which is primarily used for classification problems. Random Forest regression is designed to predict continuous numerical values rather than discrete classes.
📄️ Logistic Regression
Logistic regression is a statistical technique used for binary classification problems. It is commonly used when the dependent variable, or the target variable, is binary or categorical in nature, and the goal is to predict the probability of an instance belonging to a particular class.