The C4.5 decision tree algorithm is a popular method used in machine learning for classification tasks. It is an extension of the earlier ID3 algorithm developed by Ross Quinlan. C4.5 constructs a decision tree based on a set of training data and is known for its ability to handle both categorical and continuous data, manage missing values, and perform pruning to avoid overfitting.
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Splitting Criterion: C4.5 uses the concept of information gain ratio (a normalized version of information gain) to determine the best attribute to split the data at each node of the tree.
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Handling Continuous Data: For continuous attributes, C4.5 creates a threshold and splits the data into subsets where values are either above or below this threshold.
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Handling Missing Values: C4.5 can handle missing attribute values by considering the proportion of each class in the subset of data where the attribute is not missing.
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Pruning: C4.5 performs a pruning step after the tree is created to remove branches that may reflect noise or outliers, improving the generalization ability of the model.
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Calculate Gain Ratio (GR) for each feature and select the best feature:
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For each feature:
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Calculate the entropy before the split.
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Calculate the entropy after the split for each possible value.
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Calculate the Information Gain.
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Normalize the Information Gain to get the Gain Ratio.
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Select the feature with the highest Gain Ratio.
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Create a node for the best feature:
- This node will represent the decision based on the best feature selected.
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Split the dataset based on the unique values of the best feature:
- Partition the dataset into subsets where each subset contains all the instances with a specific value of the chosen feature.
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Loop to check each unique value in the best feature:
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For each unique value of the chosen feature:
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Create a subset of the dataset based on the unique value:
- Extract all instances that have this unique value for the feature.
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If the labels of all samples in the subset are the same:
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Create a leaf node:
- This node will have a class label which is the label of the samples.
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Otherwise:
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Return to the first step:
- Recursively apply the C4.5 algorithm to the subset to build the tree further.
- Recursively apply the C4.5 algorithm to the subset to build the tree further.
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