What is the difference between Decision Trees and Random Forest. Why do we grow a lot of Decision Trees in a Random Forest ?

Question

Accepted Answer

Decision Trees are single trees while Random Forest is a collection of trees. Random Forest grows multiple trees to improve accuracy and reduce overfitting.

Decision Trees are individual trees that make decisions based on features of the data.
Random Forest is an ensemble method that combines multiple Decision Trees to improve accuracy and reduce overfitting.
Random Forest grows a lot of Decision Trees to increase diversity in predictions and reduce the risk of overfitting.
Each Decision Tree in a Random Forest is trained on a random subset of the data and features, leading to different trees making different decisions.
By averaging the predictions of multiple trees in a Random Forest, we can achieve better overall performance compared to a single Decision Tree.

What is the difference between Decision Trees and Random Forest. Why do we grow a lot of Decision Trees in a Random Forest ?

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