C5

As an enhancement to the ID3 algorithm, Ross Quinlan created the decision tree algorithm C4.5. In machine learning and data mining applications, it is a well-liked approach for creating decision trees. Certain drawbacks of the ID3 algorithm are addressed in C4.5, including its incapacity to deal with continuous characteristics and propensity to overfit the training set.

A modification of information gain known as the gain ratio is used to address the bias towards qualities with many values. It is computed by dividing the information gain by the intrinsic information, which is a measurement of the quantity of data required to characterize an attribute’s values.

[Tex]Gain Ratio = \frac{Split\; gain}{Gain\;\;information} [/Tex]

Where Split Information represents the entropy of the feature itself. The feature with the highest gain ratio is chosen for splitting.

When dealing with continuous attributes, C4.5 sorts the attribute’s values first, and then chooses the midpoint between each pair of adjacent values as a potential split point. Next, it determines which split point has the largest value by calculating the information gain or gain ratio for each.

By turning every path from the root to a leaf into a rule, C4.5 can also produce rules from the decision tree. Predictions based on fresh data can be generated using the rules.

C4.5 is an effective technique for creating decision trees that can produce rules from the tree and handle both discrete and continuous attributes. The model’s accuracy is increased and overfitting is prevented by its utilization of gain ratio and decreased error pruning. Nevertheless, it might still be susceptible to noisy data and might not function effectively on datasets with a lot of features.

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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C4.5

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CHAID (Chi-Square Automatic Interaction Detection)

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Conclusion

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