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This tutorial focuses on the Decision tree algorithm used in classification algorithms
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Let's discuss the process of
drawing a decision tree, interpreting attributes like pressure and beauty, and emphasizing the
importance of calculating information effectively
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Introduction to Decision Trees
Decision trees are a type of machine-learning algorithm that can be used for both classification
and regression tasks
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These rules can then be used to predict the value of the target variable for new data
samples
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The algorithm works by recursively splitting the data into smaller and smaller subsets based on
the feature values
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Components of a Decision Tree
Before we dive into the types of Decision Tree Algorithms, we need to know about the following
important terms:
Root Node: It is the topmost node in the tree, which represents the complete dataset
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Internal Node: A node that symbolizes a choice regarding an input feature
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Leaf/Terminal Node: A node without any child nodes that indicates a class label or a numerical
value
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Child Node: The nodes that emerge when a parent node is split
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Information gain of outlook is high so the first node is outlook

Except outlook re-write the data set

Humidity has high information gain

Atlast wind is the last node



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