Why is linear regression better than random forest?

When there are large number of features with less data-sets(with low noise), linear regressions may outperform Decision trees/random forests. In general cases, Decision trees will be having better average accuracy. For categorical independent variables, decision trees are better than linear regression.

Can logistic regression performing better than random forest?

Logistic regression performs better when the number of noise variables is less than or equal to the number of explanatory variables and the random forest has a higher true and false positive rate as the number of explanatory variables increases in a dataset.

Why do we choose linear regression?

Linear regression analysis is used to predict the value of a variable based on the value of another variable. The variable you want to predict is called the dependent variable.

How does random forest regression predict?

Random Forest Regression is a supervised learning algorithm that uses ensemble learning method for regression. A Random Forest operates by constructing several decision trees during training time and outputting the mean of the classes as the prediction of all the trees.

How does random forest work for regression?

Random forest is a type of supervised learning algorithm that uses ensemble methods (bagging) to solve both regression and classification problems. The algorithm operates by constructing a multitude of decision trees at training time and outputting the mean/mode of prediction of the individual trees.

Does random forest outperform decision tree?

Random forests consist of multiple single trees each based on a random sample of the training data. They are typically more accurate than single decision trees. The following figure shows the decision boundary becomes more accurate and stable as more trees are added.