Implement sample code for decision tree algorithms using MapReduce | S-Logix

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How to Implement Decision Tree Algorithms Using MapReduce

Implementing Decision Tree Algorithms Using MapReduce

Steps for Implementing Decision Tree Algorithms

Description:
The decision tree algorithm (C4.5) builds classification models in a tree structure. This process is implemented using MapReduce, where the first MapReduce function counts the occurrences of attributes with class labels. The second function computes the Gain Ratio by calculating Information Gain and Split Information. Finally, the third MapReduce function selects the best attributes for constructing the decision tree.

Steps for Implementing Decision Tree Algorithms Using MapReduce

Step 1: First MapReduce - Count the Attribute for a Specific Class Label

Map Phase:
Input: <k1, v1>
k1: Line number
v1: Record
Process: Extract the attribute and class label from each record.
Output: <k2, v2>
k2: Attribute with class label
v2: 1 (for each occurrence of the attribute with class label)

Reduce Phase:
Input: <k2, List<v2>>
Count the number of occurrences of the combination (attribute with class label).
Output: <k3, v3>
k3: Attribute with class label
v3: Frequency (count of occurrences)

Step 2: Second MapReduce - Find the Best Splitting Attribute (Decision Node)

Map Phase:
Input: <k3, v3>
Compute the Entropy, Information Gain, and Split Information for each attribute.
Output: <k4, v4>
k4: Attribute
v4: Entropy, Information Gain, and Split Information

Reduce Phase:
Input: <k4, List<v4>>
Calculate the Information Gain Ratio for each attribute.
Output: <k5, v5>
k5: Decision node (best splitting attribute)
v5: Information Gain Ratio

Step 3: Third MapReduce - Tree Construction

Map Phase:
Input: <k5, v5>
Read the record of the best attribute and compute the node ID for the highest attribute.
Output: <k6, v6>
k6: Node ID
v6: Elements (attribute values)
This process recursively calls for creating non-leaf branches until all data is classified.

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