- Decision tree algorithm belongs to supervised learning which solves both classifications and Regression problems in machine learning.
- Decision tree solves problems using a tree-type structure which has leaves, decision nodes, and links between nodes. Each node represents an attribute or feature, each branch of the tree represent the decision, and each leaf represents the outcomes.
- Decision tree algorithm often mimic human thinking hence, it can be easily understood as compared to other classifications algorithm.
Difference between Decision Tree and Random Forest algorithm:
| Decision Tree Algorithm | Random Forest Algorithm |
|---|---|
| Decision tree algorithm is a tree-like structure to solve classification and regression problems. | Random forest algorithm is a combination of various decision trees which gives the final output based on the average of each tree output. |
| Decision tree may have a chance of Overfitting problem. | Random Forest reduces the chance of Overfitting problem by averaging out several trees predictions. |
| Simpler to understand as it is based on human thinking. | This algorithm is comparatively complex. |
| It gives less accurate result as compared to the random forest algorithm. | It gives a more accurate result. |
