What is Iterative Dichotomiser3 Algorithm?
ID3 or Iterative Dichotomiser3 Algorithm is used in machine learning for building decision trees from a given dataset. It was developed in 1986 by Ross Quinlan. It is a greedy algorithm that builds a decision tree by recursively partitioning the data set into smaller and smaller subsets until all data points in each subset belong to the same class. It employs a top-down approach, recursively selecting features to split the dataset based on information gain.
Thе ID3 (Iterative Dichotomiser 3) algorithm is a classic decision tree algorithm used for both classification and regression tasks.ID3 deals primarily with categorical properties, which means that it can efficiently handle objects with a discrete set of values. This property is consistent with its suitability for problems where the input features are categorical rather than continuous.One of the strengths of ID3 is its ability to generate interpretable decision trees. The resulting tree structure is easily understood and visualized, providing insight into the decision-making process. However, ID3 can be sensitive to noisy data and prone to overfitting, capturing details in the training data that may not adequately account for new unseen data.
How ID3 Algorithms work?
The ID3 algorithm works by building a decision tree, which is a hierarchical structure that classifies data points into different categories and splits the dataset into smaller subsets based on the values of the features in the dataset. The ID3 algorithm then selects the feature that provides the most information about the target variable. The decision tree is built top-down, starting with the root node, which represents the entire dataset. At each node, the ID3 algorithm selects the attribute that provides the most information gain about the target variable. The attribute with the highest information gain is the one that best separates the data points into different categories.
ID3 metrices
The ID3 algorithm utilizes metrics related to information theory, particularly entropy and information gain, to make decisions during the tree-building process.
Information Gain and Attribute Selection
The ID3 algorithm uses a measure of impurity, such as entropy or Gini impurity, to calculate the information gain of each attribute. Entropy is a measure of disorder in a dataset. A dataset with high entropy is a dataset where the data points are evenly distributed across the different categories. A dataset with low entropy is a dataset where the data points are concentrated in one or a few categories.
[Tex]H(S)= Σ -(P_i * log_2(P_i)) [/Tex]
- where, [Tex]P_i [/Tex] represents the fraction of the sample within a particular node.
- S – The current dataset.
- i – Set of classes in S
If entropy is low, data is well understood; if high, more information is needed. Preprocessing data before using ID3 can enhance accuracy. In sum, ID3 seeks to reduce uncertainty and make informed decisions by picking attributes that offer the most insight in a dataset.
Information gain assesses how much valuable information an attribute can provide. We select the attribute with the highest information gain, which signifies its potential to contribute the most to understanding the data. If information gain is high, it implies that the attribute offers a significant insight. ID3 acts like an investigator, making choices that maximize the information gain in each step. This approach aims to minimize uncertainty and make well-informed decisions, which can be further enhanced by preprocessing the data.
[Tex]IG(A,D) = H(S) – \sum_v \frac{|S_v|}{|S|} \times H(S_v)] [/Tex]
- where, [Tex]| S| [/Tex] is the total number of instances in dataset.
- [Tex]|S_v| [/Tex] is the number of instances in dataset for which attribute D has values v.
- [Tex]H(S) [/Tex] is the entropy of dataset.
What are the steps in ID3 algorithm?
- Determine entropy for the overall the dataset using class distribution.
- For each feature.
- Calculate Entropy for Categorical Values.
- Assess information gain for each unique categorical value of the feature.
- Choose the feature that generates highest information gain.
- Iteratively apply all above steps to build the decision tree structure.
Sklearn | Iterative Dichotomiser 3 (ID3) Algorithms
The ID3 algorithm is a popular decision tree algorithm used in machine learning. It aims to build a decision tree by iteratively selecting the best attribute to split the data based on information gain. Each node represents a test on an attribute, and each branch represents a possible outcome of the test. The leaf nodes of the tree represent the final classifications. In this article, we will learn how to use the ID3 algorithm to build a decision tree to predict the output in detail.