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Bharani Kumar Depuru is a well known IT personality from Hyderabad. He is the Founder and Director of AiSPRY 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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To develop a Decision Tree, consider 2 important questions:
Q1. Which variable to split
Q2. When to stop growing the Tree
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When an event occurs frequently, it has extremely little informational value.
Therefore, "Information Content is Proportional to Rarity"
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Purity = Accuracy = 1 - Entropy
In Accuracy we assign the Dominant Label to each region.
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.
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.
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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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.
Stopping the tree from growing once the desired condition is meet.
Disadvantage: When to stop the tree from growing. What if an important pattern was prevented from learning?
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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