Decision Tree Representation in Machine Learning

What are decision tree and decision tree learning? Explain the representation of the decision tree with an example.

Decision Trees is one of the most widely used Classification Algorithm

Features of Decision Tree Learning

Method for approximating discrete-valued functions (including boolean)

Learned functions are represented as decision trees (or if-then-else rules)

Expressive hypotheses space, including disjunction

The decision tree is robust to noisy data

Representation of Decision Tree Learning

Decision trees classify instances by sorting them down the tree from the root to some leaf node, which provides the classification of the instance.

Each node in the tree specifies a test of some attribute of the instance, and each branch descending from that node corresponds to one of the possible values for this attribute.

An instance is classified by starting at the root node of the tree, testing the attribute specified by this node, then moving down the tree branch corresponding to the value of the attribute in the given example.

See also  Implementation of Linear and Polynomial Regression in Python

This process is then repeated for the subtree rooted at the new node.

In general, decision trees represent a disjunction of conjunctions of constraints on the attribute values of instances.

Each path from the tree root to a leaf corresponds to a conjunction of attribute tests, and the tree itself to a disjunction of these conjunctions.

Example:

Decision Tree for the above data set:

The rules derived from the decision tree are:

1. Outlook=Sunny, Humidity=High –>  No

2. Outlook=Sunny, Humidity=Normal –>  Yes

3. Outlook=Overcast –>  Yes

4. Outlook=Rain, Wind=Strong –>  No

5. Outlook=Rain, Wind=Weak–>  Yes

Now, The new instance is classified using the above rules.

Example:

<Outlook=Sunny, Temp=Hot, Humidity=High, Wind=Strong> —>  No

Solved Numerical Examples and Tutorial on Decision Trees Machine Learning:

1. How to build a decision Tree for Boolean Function Machine Learning

2. How to build a decision Tree for Boolean Function Machine Learning

3. How to build Decision Tree using ID3 Algorithm – Solved Numerical Example – 1

4. How to build Decision Tree using ID3 Algorithm – Solved Numerical Example -2

5. How to build Decision Tree using ID3 Algorithm – Solved Numerical Example -3

6. Appropriate Problems for Decision Tree Learning Machine Learning Big Data Analytics

7. How to find the Entropy and Information Gain in Decision Tree Learning

8. Issues in Decision Tree Learning Machine Learning

9. How to Avoid Overfitting in Decision Tree Learning, Machine Learning, and Data Mining

10. How to handle Continuous Valued Attributes in Decision Tree Learning, Machine Learning

Summary

This tutorial discusses how to Implement and demonstrate the Decision Tree ID3 Algorithm in Python. The training data is read from a .CSV file. If you like the tutorial share it with your friends. Like the Facebook page for regular updates and YouTube channel for video tutorials.