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Decision Tree

  • July 15, 2023
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Meet the Author : Mr. Bharani Kumar

Bharani Kumar Depuru is a well known IT personality from Hyderabad. He is the Founder and Director of Innodatatics Pvt Ltd and 360DigiTMG. Bharani Kumar is an IIT and ISB alumni with more than 18+ years of experience, he held prominent positions in the IT elites like HSBC, ITC Infotech, Infosys, and Deloitte. He is a prevalent IT consultant specializing in Industrial Revolution 4.0 implementation, Data Analytics practice setup, Artificial Intelligence, Big Data Analytics, Industrial IoT, Business Intelligence and Business Management. Bharani Kumar is also the chief trainer at 360DigiTMG with more than Ten years of experience and has been making the IT transition journey easy for his students. 360DigiTMG is at the forefront of delivering quality education, thereby bridging the gap between academia and industry.

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“Categorical Variable Decision Tree” When output is Categorical
“Continuous Variable Decision Tree” When output is Numerical

 

Decision Trees are

  • Nonparametric hierarchical model, that works on divide & conquer strategy
  • Rule-based algorithm that works on the principle of recursive partitioning. A path from root node to leaf node represents a rule
  • Tree-like structure in which an internal node represents a test on an attribute, each branch represents outcome of test and each leaf node represents the class label

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Decision Trees Decision Trees

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A Greedy Algorithm

To develop a Decision Tree, consider 2 important questions:

Q1. Which variable to split

Q2. When to stop growing the Tree

Decision Trees Decision Trees

Decision Trees
Decision Trees
Decision Trees

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Decision Tree

Information Theory 101

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When an event occurs frequently, it has extremely little informational value.Decision Trees

Therefore, "Information Content is Proportional to Rarity"Decision Trees

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Entropy:

  • Entropy is the expected information content of all the events
  • Entropy value of 0 means the sample is completely homogeneous
  • Entropy value of 1 means the sample is completely heterogeneous

Decision Trees

Purity = Accuracy = 1 - Entropy

In Accuracy we assign the Dominant Label to each region.Decision Trees

Decision Trees

Each area is given the Dominant Label in Accuracy.

Decision trees identify qualities that yield the most homogenous branches by measuring the entropy, which is a measure of disorder or impurity (variation/heterogeneity).

The GINI Measure, which is an expected measurement of purity, can also be used. With probabilistic labelling, accuracy.

Decision Trees
Decision Trees

One must choose which characteristic to divide after determining the purity measure. To do this, one needs quantify the change in homogeneity brought on by a feature split. Information Gain is the name given to this computation.

Entropy in the segment before the split (S1) and the partitions that resulted from the split (S2) are compared to determine the information gain of a feature.

Decision Trees

Better for the attribute is less variety in class labels after the split.

Gain in information: A decrease in entropy (variation) as a result of dividing the dataset along an attribute.

Greater information gain indicates greater uniformity.

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Decision Trees

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Pros and Cons of Decision Tree

Strengths Weaknesses
Uses the important feature during decision making Biased towards factors (features), which have a lot of levels
Interpretation is very simple because there is no mathematical background needed Small changes in the data will result in large changes to decision making

Regularisation techniques can be used to combat model overfitting.

The regularisation method employed in the Decision Tree is pruning.

Reducing the tree's size in order to generalise previously unobserved data is known as pruning.

Two Pruning techniques are

Pre-Pruning or Early Stopping

Stopping the tree from growing once the desired condition is meet.

  • Stop the tree from growing once it reaches a certain number of decisions
  • Stop the tree from growing if decision nodes contain only a small number of examples

Disadvantage: When to stop the tree from growing. What if an important pattern was prevented from learning?

Post-Pruning

Grows the tree completely and then apply the conditions to reduce the tree size.

Example, if the error rate is less than 3% then reduce the nodes.

So, the nodes and branches that have less reduction of errors are removed.

This process of grafting branches is known as subtree raising or subtree replacement.

Post-Pruning is more effective than Pre-Pruning

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