Explain Classification by Decision Tree Induction with its example.

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CLASSIFICATION BY DECISION TREE INDUCTION

Decision Tree and Decision tree induction: Decision tree induction is the learning of decision trees from class labeled training tuples. The Decision tree is a flow-chart-like tree structure in which the internal node denotes a test on an attribute, the branch represents an outcome of the test, and leaf nodes represent class labels as shown in the figure below.

Figure 6.5: Decision tree to classify whether a person is infected or not infected.

To use decision trees for classification attributes of a new tuple to be classified are tested against the decision tree and a path is traced from the root to a leaf node that holds the class label for that tuple. 

The decision tree classifies this new data tuple by checking attribute value starting from the root to leaf node so, initially checks the root attribute Breathing issue value in the new data tuple, here, value is YES, so the decision tree follows the left branch from the root and reached the next level attribute node Fever and again makes a check and found the Fever attribute value YES. Therefore, again follows the left branch and reached the leaf node labeled infected. Hence, assigns the class label infected =YES to this new data tuple.Classification by Decision Tree Induction


                              OR,

Classification by Decision Tree Induction

Decision tree induction is the learning of decision trees from class labeled training tuples. It is a decision tree is a flowchart-like tree structure where internal nodes (non-leaf node) denotes a test on an attribute branches represent outcomes of tests Leaf nodes (terminal nodes) hold class labels and the Root node is the topmost node.



Why decision trees classifiers are so popular?

  • The construction of a decision tree does not require any domain
  • knowledge or parameter setting
  • They can handle high dimensional data
  • The intuitive representation that is easily understood by humans
  • Learning and classification are simple and fast
  • They have a good accuracy


Algorithm for constructing Decision Tress

Constructing a Decision tree uses a greedy algorithm. The tree is constructed in a top-down recursive divide-and-conquer manner.

1. At the start, all the training tuples are at the root

2. Tuples are partitioned recursively based on selected attributes

3. If all samples for a given node belong to the same class

- Label the class

4. If There are no remaining attributes for further partitioning

- Majority voting is employed for classifying the leaf

5. There are no samples left

- Label the class and terminate

6. Else

- Got to step 2


Types of Decision Tree Induction

a) Basic algorithm (a greedy algorithm)

– Tree is constructed in a top-down recursive divide-and-conquer manner

– At start, all the training examples are at the root

– Examples are partitioned recursively to maximize purity


b) Conditions for stopping partitioning

– All samples belong to the same class

– Leaf node smaller than a specified threshold

– Tradeoff between complexity and generalizability


c) Predictions for new data:

– Classification by majority voting is employed for classifying all members of the leaf

– Probability-based on training data that ended up in that leaf.

– Class Probability estimates can be used also






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