Glossary of Regression
Here is a glossary of some common terms used in regression machine learning:
Regression: A type of machine learning problem where the goal is to predict a continuous value for a given input.
Linear Regression: A regression model that assumes a linear relationship between the input variables and the output variable.
Polynomial Regression: A type of regression model that fits a polynomial curve to the data, allowing for nonlinear relationships between the input variables and the output variable.
Multivariate Regression: A type of regression model that involves predicting a continuous output variable using two or more input variables.
Regularization: A technique used to prevent overfitting in regression models by adding a penalty term to the loss function, which discourages large weights in the model.
Mean Squared Error (MSE): A common metric used to evaluate the performance of regression models, calculated as the average of the squared differences between the predicted and actual values.
R-squared: Another commonly used metric for evaluating regression models that measures the proportion of the variance in the output variable that can be explained by the input variables.
Gradient Descent: A technique used to optimize the parameters of a regression model by iteratively adjusting them in the direction of the steepest descent of the loss function.
Support Vector Regression (SVR): A type of regression model that uses support vector machines to predict a continuous output variable.
Random Forest Regression: An ensemble learning algorithm that constructs multiple decision trees and combines their predictions to improve the accuracy and robustness of predictions in regression problems.
Extra Trees: An ensemble learning algorithm that is similar to Random Forest, but uses a different strategy for selecting the splitting points in the decision trees. Extra Trees randomly choose splitting points, whereas Random Forest evaluates different splitting points to find the best one.
KMeansFeatures: A feature engineering technique that involves clustering the input data using the K-Means algorithm, and then using the resulting cluster assignments as features in a regression model.
XGBoost: A popular gradient boosting library that uses a combination of decision trees and regularization to make accurate and robust predictions in regression problems.
These are just a few of the terms and techniques used in regression machine learning. As with any field, there are many more specialized terms and techniques that can be learned with further study and practice.
Regression: A type of machine learning problem where the goal is to predict a continuous value for a given input.
Linear Regression: A regression model that assumes a linear relationship between the input variables and the output variable.
Polynomial Regression: A type of regression model that fits a polynomial curve to the data, allowing for nonlinear relationships between the input variables and the output variable.
Multivariate Regression: A type of regression model that involves predicting a continuous output variable using two or more input variables.
Regularization: A technique used to prevent overfitting in regression models by adding a penalty term to the loss function, which discourages large weights in the model.
Mean Squared Error (MSE): A common metric used to evaluate the performance of regression models, calculated as the average of the squared differences between the predicted and actual values.
R-squared: Another commonly used metric for evaluating regression models that measures the proportion of the variance in the output variable that can be explained by the input variables.
Gradient Descent: A technique used to optimize the parameters of a regression model by iteratively adjusting them in the direction of the steepest descent of the loss function.
Support Vector Regression (SVR): A type of regression model that uses support vector machines to predict a continuous output variable.
Random Forest Regression: An ensemble learning algorithm that constructs multiple decision trees and combines their predictions to improve the accuracy and robustness of predictions in regression problems.
Extra Trees: An ensemble learning algorithm that is similar to Random Forest, but uses a different strategy for selecting the splitting points in the decision trees. Extra Trees randomly choose splitting points, whereas Random Forest evaluates different splitting points to find the best one.
KMeansFeatures: A feature engineering technique that involves clustering the input data using the K-Means algorithm, and then using the resulting cluster assignments as features in a regression model.
XGBoost: A popular gradient boosting library that uses a combination of decision trees and regularization to make accurate and robust predictions in regression problems.
These are just a few of the terms and techniques used in regression machine learning. As with any field, there are many more specialized terms and techniques that can be learned with further study and practice.
Updated on: 21/02/2023
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