ID3 (Iterative Dichotomiser 3)
An approach for decision trees called ID3 (Iterative Dichotomiser 3) is employed in classification applications. It is one of the first and most used decision tree algorithms, created by Ross Quinlan in 1986. The ID3 algorithm builds a decision tree from a given dataset using a greedy, top-down methodology.
It works by greedily choosing the feature that maximizes the information gain at each node. ID3 calculates entropy and information gain for each feature and selects the feature with the highest information gain for splitting.
ID3 uses entropy to measure the uncertainty or disorder in a dataset. Entropy, denoted by H(D) for dataset D, is calculated using the formula:
[Tex]H(D) = \Sigma^n _{i=1}\;p_{i}\; log_{2}(p_{i}) [/Tex]
Information gain quantifies the reduction in entropy achieved by splitting the data based on a particular feature. Features with higher information gain are preferred for splitting. Information gain is calculated as follows:
[Tex]Information\; Gain = H(D) – \Sigma^V_{v=1} \frac{|D_{v}|}{|D|}H (D_{v})
[/Tex]
Every decision tree node’s dataset is recursively divided using the ID3 algorithm according to the chosen attribute. This method keeps going until either there are no more attributes to divide on, or all the examples in a node belong to the same class.
The decision tree may be trimmed after it is constructed in order to enhance generalization and lessen overfitting. In order to do this, nodes that do not considerably improve the correctness of the tree must be removed.
A couple of the ID3 algorithm’s drawbacks are that it tends to overfit the training set and cannot directly handle continuous attributes. Owing to these drawbacks, other decision tree algorithms that address some of these problems have been developed, including C4.5 and CART.
Entropy, information gain, and recursive partitioning are three key principles in the ID3 algorithm, which is a fundamental technique for creating decision trees. Mastering these ideas is crucial to learning about decision tree algorithms in machine learning.
Decision Tree Algorithms
Decision trees are a type of machine-learning algorithm that can be used for both classification and regression tasks. They work by learning simple decision rules inferred from the data features. These rules can then be used to predict the value of the target variable for new data samples.
Decision trees are represented as tree structures, where each internal node represents a feature, each branch represents a decision rule, and each leaf node represents a prediction. The algorithm works by recursively splitting the data into smaller and smaller subsets based on the feature values. At each node, the algorithm chooses the feature that best splits the data into groups with different target values.
Table of Content
- Understanding Decision Trees
- Components of a Decision Tree
- Working of the Decision Tree Algorithm
- Understanding the Key Mathematical Concepts Behind Decision Trees
- Types of Decision Tree Algorithms
- ID3 (Iterative Dichotomiser 3)
- C4.5
- CART (Classification and Regression Trees)
- CHAID (Chi-Square Automatic Interaction Detection)
- MARS (Multivariate Adaptive Regression Splines)
- Implementation of Decision Tree Algorithms
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