Sure, let's start with the Decision Tree.

A Decision Tree is a flowchart-like structure in which each internal node represents a feature (or attribute), each branch represents a decision rule, and each leaf node represents an outcome. The topmost node in a decision tree is known as the root node. It learns to partition on the basis of the attribute value. It partitions the tree recursively in a manner called recursive partitioning.

Real-time example of Decision Tree: Let's say you want to predict whether a person will buy a car or not. You have some data about the person like Age, Gender, Salary, and whether that person bought a car or not in the past. You can create a decision tree with this data. The decision tree will start with the root node, let's say Age. If Age is less than 18, the person will not buy a car (leaf node). If Age is more than 18, then we check the next attribute, let's say Salary. If Salary is less than $2000, the person will not buy a car. If Salary is more than $2000, the person will buy a car. This is a very simple example of how a decision tree works.

Now, let's move to Random Forest.

Random Forest is a collection of Decision Trees. It creates different random subsets of the given data and builds a decision tree for each subset. Then, it aggregates the votes from different decision trees to decide the final class of the test object.

Real-time example of Random Forest: Let's take the same example of predicting whether a person will buy a car or not. But this time, instead of creating one decision tree, we will create multiple decision trees with different subsets of data. One decision tree might consider Age and Salary, another might consider Gender and Salary, and so on. When a new person comes in, all the decision trees in the Random Forest will make a prediction and the final prediction will be based on the majority votes. For example, if we have 100 trees and 70 trees predict that the person will buy a car and 30 trees predict that the person will not buy a car, the final prediction will be that the person will buy a car. This is how a Random Forest works.

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Sure, let's start with the Decision Tree.

A Decision Tree is a flowchart-like structure in which each internal node represents a feature (or attribute), each branch represents a decision rule, and each leaf node represents an outcome. The topmost node in a decision tree is known as the root node. It learns to partition on the basis of the attribute value. It partitions the tree recursively in a manner called recursive partitioning.

Real-time example of Decision Tree: Let's say you want to predict whether a person will buy a car or not. You have some data about the person like Age, Gender, Salary, and whether that person bought a car or not in the past. You can create a decision tree with this data. The decision tree will start with the root node, let's say Age. If Age is less than 18, the person will not buy a car (leaf node). If Age is more than 18, then we check the next attribute, let's say Salary. If Salary is less than $2000, the person will not buy a car. If Salary is more than $2000, the person will buy a car. This is a very simple example of how a decision tree works.

Now, let's move to Random Forest.

Random Forest is a collection of Decision Trees. It creates different random subsets of the given data and builds a decision tree for each subset. Then, it aggregates the votes from different decision trees to decide the final class of the test object.

Real-time example of Random Forest: Let's take the same example of predicting whether a person will buy a car or not. But this time, instead of creating one decision tree, we will create multiple decision trees with different subsets of data. One decision tree might consider Age and Salary, another might consider Gender and Salary, and so on. When a new person comes in, all the decision trees in the Random Forest will make a prediction and the final prediction will be based on the majority votes. For example, if we have 100 trees and 70 trees predict that the person will buy a car and 30 trees predict that the person will not buy a car, the final prediction will be that the person will buy a car. This is how a Random Forest works.

Knowee AI · Accepted Answer

Sure, let's start with the Decision Tree.

A Decision Tree is a flowchart-like structure in which each internal node represents a feature (or attribute), each branch represents a decision rule, and each leaf node represents an outcome. The topmost node in a decision tree is known as the root node. It learns to partition on the basis of the attribute value. It partitions the tree recursively in a manner called recursive partitioning.

Real-time example of Decision Tree: Let's say you want to predict whether a person will buy a car or not. You have some data about the person like Age, Gender, Salary, and whether that person bought a car or not in the past. You can create a decision tree with this data. The decision tree will start with the root node, let's say Age. If Age is less than 18, the person will not buy a car (leaf node). If Age is more than 18, then we check the next attribute, let's say Salary. If Salary is less than $2000, the person will not buy a car. If Salary is more than $2000, the person will buy a car. This is a very simple example of how a decision tree works.

Now, let's move to Random Forest.

Random Forest is a collection of Decision Trees. It creates different random subsets of the given data and builds a decision tree for each subset. Then, it aggregates the votes from different decision trees to decide the final class of the test object.

Real-time example of Random Forest: Let's take the same example of predicting whether a person will buy a car or not. But this time, instead of creating one decision tree, we will create multiple decision trees with different subsets of data. One decision tree might consider Age and Salary, another might consider Gender and Salary, and so on. When a new person comes in, all the decision trees in the Random Forest will make a prediction and the final prediction will be based on the majority votes. For example, if we have 100 trees and 70 trees predict that the person will buy a car and 30 trees predict that the person will not buy a car, the final prediction will be that the person will buy a car. This is how a Random Forest works.

decision tree and random forest with a real -time example

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