How does a decision tree work?(1 Point)Minimizes the information gain and maximizes the entropyMaximizes the information gain and minimizes the entropyMinimizes the information gain and minimizes the entropyMaximizes the information gain and maximizes the entropy

A decision tree works by maximizing the information gain and minimizing the entropy.

Here's a step-by-step explanation:

1. The decision tree algorithm starts at the root of the tree. It examines all the attributes and chooses the one that results in the highest information gain, or in other words, the one that provides the most useful information for making a decision. This attribute becomes the decision node.

2. The dataset is then split based on the values of the chosen attribute. This process is repeated for each of the resulting subsets, with the algorithm choosing the next attribute that maximizes the information gain for each subset.

3. This process continues until the tree is fully grown. The final decision nodes (leaves of the tree) represent the predictions or decisions based on the input attributes.

4. 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. The decision rules are generally in form of if-then-else statements.

The entropy is a measure of the impurity or disorder. The decision tree algorithm aims to continue splitting the data in a way that decreases the entropy, meaning the data becomes more organized and the decision becomes more clear.

So, the correct answer is: Maximizes the information gain and minimizes the entropy.