Decision Tree

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Decision Tree Classification Algorithm

  • A decision Tree is a Supervised learning technique that can be used for both classification and Regression problems, but mostly it is preferred for solving Classification problems. It is a tree-structured classifier, where internal nodes represent the features of a dataset, branches represent the decision rules and each leaf node represents the outcome.
  • In a Decision tree, there are two nodes, which are the Decision Node and Leaf Node. Decision nodes are used to make any decision and have multiple branches, whereas Leaf nodes are the output of those decisions and do not contain any further branches.
  • The decisions or the test are performed on the basis of features of the given dataset.
  • It is a graphical representation for getting all the possible solutions to a problem/decision based on given conditions.
  • It is called a decision tree because similar to a tree, it starts with the root node, which expands on further branches and constructs a tree-like structure.
  • In order to build a tree, we use the CART algorithm, which stands for Classification and Regression Tree algorithm.
  • A decision tree simply asks a question and based on the answer (Yes/No), it further split the tree into subtrees.   


How does the Decision Tree algorithm Work?

  • Step-1: Begin the tree with the root node, says S, which contains the complete dataset.
  • Step-2: Find the best attribute in the dataset using Attribute Selection Measure (ASM).
  • Step-3: Divide the S into subsets that contains possible values for the best attributes.
  • Step-4: Generate the decision tree node, which contains the best attribute.
  • Step-5: Recursively make new decision trees using the subsets of the dataset created in step -3. Continue this process until a stage is reached where you cannot further classify the nodes and called the final node as a leaf node

Attribute Selection Measures

While implementing a Decision tree, the main issue arises that how to select the best attribute for the root node and for sub-nodes. So, to solve such problems there is a technique which is called as Attribute selection measure or ASM. By this measurement, we can easily select the best attribute for the nodes of the tree. There are two popular techniques for ASM, which are:

  • Information Gain
  • Gini Index

1. Information Gain:

  • Information gain is the measurement of changes in entropy after the segmentation of a dataset based on an attribute.
  • It calculates how much information a feature provides us about a class.
  • According to the value of information gain, we split the node and build the decision tree.
  • A decision tree algorithm always tries to maximize the value of information gain, and a node/attribute having the highest information gain is split first. It can be calculated using the below formula:
  1. Information Gain= Entropy(S)- [(Weighted Avg) *Entropy(each feature)  


Entropy: Entropy is a metric to measure the impurity in a given attribute. It specifies randomness in data. Entropy can be calculated as:

Entropy(s)= -P(yes)log2 P(yes)- P(no) log2 P(no)

Where,

  • S= Total number of samples
  • P(yes)= probability of yes
  • P(no)= probability of no

2. Gini Index:

  • Gini index is a measure of impurity or purity used while creating a decision tree in the CART(Classification and Regression Tree) algorithm.
  • An attribute with the low Gini index should be preferred as compared to the high Gini index.
  • It only creates binary splits, and the CART algorithm uses the Gini index to create binary splits.
  • Gini index can be calculated using the below formula: Gini Index= 1- ∑jPj2

Comments

  1. Alan1 February 2024 at 03:33

    The blog provides a clear and concise exploration of decision trees in data mining, making it an invaluable resource for enthusiasts and professionals alike. It adeptly breaks down the complexities of decision tree models, offering a comprehensive understanding of their application in data mining. The content is structured in an informative manner, simplifying the intricacies of decision tree algorithms. For anyone delving into data mining or seeking to comprehend decision tree methodologies, this blog proves to be a valuable guide. It effectively demystifies the concept, making decision trees accessible to a broad audience interested in leveraging them for insightful data analysis.

    ReplyDelete

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