Regression

Functional Gradient Boosting is a machine learning technique that builds models in a stage-wise manner to improve prediction accuracy.

Gradient Boosting Regressor is a machine learning algorithm for regression that builds models in a stage-wise fashion.

Huber Loss is a loss function used in regression that is less sensitive to outliers than mean squared error.

Isotonic regression is a statistical technique for fitting a non-decreasing function to data.

K-Nearest Neighbors (KNN) is a simple algorithm used for classification and regression based on the closest training examples.

A linear model uses linear relationships to predict outcomes based on input variables.

Logit is a function used to model binary outcomes in statistics and machine learning.

Model regression is a statistical technique used to predict the value of a dependent variable based on one or more independent variables.

MSE Loss measures the average squared differences between predicted and actual values in regression tasks.

Multi-Target Regression predicts multiple outputs from a single input using statistical and machine learning techniques.

Multi-variable regression analyzes the relationship between multiple independent variables and a dependent variable.

Multivariate regression analyzes the relationship between multiple independent variables and a dependent variable.

Non-linear regression models relationships that aren't straight lines, capturing complex patterns in data.

Ordinary Least Squares (OLS) is a regression analysis technique used to estimate the relationship between variables.

Orthogonal Distance Regression minimizes the orthogonal distances from points to a regression model, enhancing accuracy in multivariate data.

Parameter Regression is a statistical method for predicting outcomes based on input features and their associated parameters.

Parametric regression is a statistical method that models relationships using predefined equations with parameters.

Support Vector Machines are supervised learning models used for classification and regression tasks in machine learning.