decision tree explain
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What is a Decision Tree?
Let’s start with a story. Suppose you have a business and you want to acquire some new customers. You also have a limited budget, and you want to ensure that, in advertising, you focus on customers who are the most likely to be converted.
How do you figure out who these people are? You need a classification algorithm that can identify these customers and one particular classification algorithm that could come in handy is the decision tree. A decision tree, after it is trained, gives a sequence of criteria to evaluate features of each new customer to determine whether they will likely be converted.
To start off, you can use data you already have on your existing customers to build a decision tree. Your data should include all the customers, their descriptive features, and a label that indicates whether they converted or not.
The idea of a decision tree is to divide the data set into smaller data sets based on the descriptive features until you reach a small enough set that contains data points that fall under one label.
Each feature of the data set becomes a root[parent] node, and the leaf[child] nodes represent the outcomes. The decision on which feature to split on is made based on resultant entropy reduction or information gain from the split.
Let’s start with a story. Suppose you have a business and you want to acquire some new customers. You also have a limited budget, and you want to ensure that, in advertising, you focus on customers who are the most likely to be converted.
How do you figure out who these people are? You need a classification algorithm that can identify these customers and one particular classification algorithm that could come in handy is the decision tree. A decision tree, after it is trained, gives a sequence of criteria to evaluate features of each new customer to determine whether they will likely be converted.
To start off, you can use data you already have on your existing customers to build a decision tree. Your data should include all the customers, their descriptive features, and a label that indicates whether they converted or not.
The idea of a decision tree is to divide the data set into smaller data sets based on the descriptive features until you reach a small enough set that contains data points that fall under one label.
Each feature of the data set becomes a root[parent] node, and the leaf[child] nodes represent the outcomes. The decision on which feature to split on is made based on resultant entropy reduction or information gain from the split.
AbhinavAtreus:
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A decision tree is a flowchart-like diagram that shows the various outcomes from a series of decisions. It can be used as a decision-making tool, for research analysis, or for planning strategy. A primary advantage for using a decision tree is that it is easy to follow and understand.
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