ANSWER

The ID3 (Iterative Dichotomiser 3) algorithm is a classic decision tree learning algorithm used for classification tasks. Here are the key steps to implement the ID3 algorithm:

1. Data Preparation:
   • Ensure your dataset is in a suitable format with binary class labels and attributes.
   • You may need to preprocess the data to handle missing values, if any.
2. Node Creation:
   • Create an empty decision tree as the root node.
3. Stopping Criteria:
   • Define stopping criteria for when to halt tree construction. Common stopping criteria include:
     • If all examples in the current node belong to the same class, mark the node as a leaf node and assign it the class label.
     • If there are no attributes left to split on, mark the node as a leaf node and assign it the majority class label of the examples in the current node.
     • You can also limit the depth of the tree to prevent overfitting.
4. Attribute Selection:
   • Implement a function to select the best attribute to split on at each node. ID3 typically uses Information Gain (IG) to measure attribute usefulness. The attribute with the highest IG is chosen.
   • Calculate Information Gain for each attribute based on the formula:
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     IG(Attribute) = H(D) - H(Attribute)


     where H(D) is the entropy of the current node’s class distribution, and H(Attribute) is the weighted average entropy of the child nodes after splitting on the attribute.

5. Splitting:
   • Split the current node into child nodes based on the selected attribute. Each child node represents a binary value (0 or 1) of the attribute.
   • Recursively apply the ID3 algorithm to each child node.
6. Tree Construction:
   • Continue the tree construction process recursively for each child node until one of the stopping criteria is met.
7. Pruning (Optional):
   • After tree construction, you can consider pruning the tree to reduce overfitting. Pruning involves removing branches that do not significantly contribute to classification accuracy.
8. Tree Output:
   • Your implementation should return the fully constructed decision tree.
9. Prediction:
   • Implement a function to make predictions using the decision tree. Traverse the tree based on attribute values to classify new instances.
10. Evaluation:
    • Evaluate the performance of your decision tree using appropriate metrics, such as accuracy, precision, recall, and F1-score, on a separate test dataset.

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