Which one can be used for both classification and regression?

[izxi]

In the machining learning methods listed below, which one can be used for both classification and regression?

Select one:

• A. Linear Regression
• B. Support Vector Machines
• C. Random Forests
• D. Decision Trees

[izxi]

Decision Trees (DTs) are a non-parametric supervised learning method used for classification and regression. The goal is to create a model that predicts the value of a target variable by learning simple decision rules inferred from the data features.

[izxi]

Support vector machines (SVMs) are a set of supervised learning methods used for classification, regression and outliers detection.

[izxi]

A random forest is a meta estimator that fits a number of decision tree classifiers on various sub-samples of the dataset and use averaging to improve the predictive accuracy and control over-fitting. The sub-sample size is always the same as the original input sample size but the samples are drawn with replacement if bootstrap=True (default).

[rysbekova]

D https://machinelearningmastery.com/classification-versus-regression-in-machine-learning/

[johnnieng]

C. Random Forests *

[om-ganesh]

@johnnieng You have asterik(*) in some of your solution ... What does that mean? Does that mean you are confident of the answer or have still some doubts?

[johnnieng]

@johnnieng You have asterik(*) in some of your solution ... What does that mean? Does that mean you are confident of the answer or have still some doubts?

Confident

Random forests or random decision forests are an ensemble learning method for classification, regression and other tasks that operate by constructing a multitude of decision trees at training time and outputting the class that is the mode of the classes (classification) or mean prediction (regression) of the individual trees.

[LSD20-crypto]

Support vector machine SVM

[iaminebriki]

D. Decision trees (most probable answer) Classification vs Regression: Some algorithms can be used for both classification and regression with small modifications, such as decision trees and artificial neural networks. Some algorithms cannot, or cannot easily be used for both problem types, such as linear regression for regression predictive modeling and logistic regression for classification predictive modeling. SVMs although can be used for both, they are mostly used for classification