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Mar 14, 2024 · There are two main types of decision trees (although there are more): Classification trees: For class label prediction. Regression trees: For continuous outcome prediction. They share common characteristics when building a tree. Let’s learn how the algorithm learn and construct a tree.
Let’s explain decision tree with examples. There are so many solved decision tree examples (real-life problems with solutions) that can be given to help you understand how decision tree diagram works.
- Important Terminology
- Working of Decision Tree
- What Is Attribute Selective Measure(Asm)?
- Gini Index
- Information Gain
- Problem Statement
- Step1: Load The Data and Finish The Cleaning Process
- Note: The Decision Tree Does Not Support Categorical Data as Features.
- Step 3: Split The Data-Set Into Train and Test Sets
- Calculation 1: Find The Entropy of The Total Dataset
Root Node: This attribute is used for dividing the data into two or more sets. The feature attribute in this node is selected based on Attribute Selection Techniques.Branch or Sub-Tree: A part of the entire decision tree is called a branch or sub-tree.Splitting: Dividing a node into two or more sub-nodes based on if-else conditions.Decision Node: After splitting the sub-nodes into further sub-nodes, then it is called the decision node.The root node feature is selected based on the results from the Attribute Selection Measure(ASM).The ASM is repeated until a leaf node, or a terminal node cannot be split into sub-nodes.Attribute Subset Selection Measure is a technique used in the data mining process for data reduction. The data reduction is necessary to make better analysis and prediction of the target variable. The two main ASM techniques are 1. Gini index 2. Information Gain(ID3)
The measure of the degree of probability of a particular variable being wrongly classified when it is randomly chosen is called the Gini index or Gini impurity. The data is equally distributed based on the Gini index. Mathematical Formula : Pi= probability of an object being classified into a particular class. When you use the Gini index as the cri...
Entropy is the main concept of this algorithm, which helps determine a feature or attribute that gives maximum information about a class is called Information gain or ID3 algorithm. By using this method, we can reduce the level of entropy from the root node to the leaf node. Mathematical Formula : ‘p’, denotes the probability of E(S), which denotes...
Predict the loan eligibility process from given data. The above problem statement is taken from Analytics Vidhya Hackathon. You can find the dataset and more information about the variables in the dataset on Analytics Vidhya. I took a classification problem because we can visualize the decision tree after training, which is not possible with regres...
There are two possible ways to either fill the null values with some value or drop all the missing values(I dropped all the missing values). If you look at the original dataset’s shape, it is (614,13), and the new data-set after dropping the null values is (480,13).
So the optimal step to take at this point is you can use feature engineering techniques like label encoding and one hot label encoding.
Why should we split the data before training a machine learningalgorithm? Please visit Sanjeev’s articleregarding training, development, test, and splitting of the data for detailed reasoning.
p = no of positive cases(Loan_Status accepted) n = number of negative cases(Loan_Status not accepted) In the data set, we have p = 332 , n=148, p+n=480 here log is with base 2 Entropy = E(s) = 0.89
Jun 28, 2021 · Stay tuned if you’d like to see Decision Trees, Random Forests and Gradient Boosting Decision Trees, explained with real-life examples and some Python code. Decision Tree is a Supervised Machine Learning Algorithm that uses a set of rules to make decisions, similarly to how humans make decisions.
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Jan 1, 2023 · In this article, we discussed a simple but detailed example of how to construct a decision tree for a classification problem and how it can be used to make predictions. A crucial step in creating a decision tree is to find the best split of the data into two subsets.
Mar 18, 2024 · In this tutorial, we’ll talk about real-world examples of tree structures. Specifically, we’ll discuss problems that arise in the areas of game development, databases, and machine learning and describe how tree structures help to solve these problems.
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Why is a decision tree a problem?
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Describe the components of a decision tree. Construct a decision tree given an order of testing the features. Determine the prediction accuracy of a decision tree on a test set. Compute the entropy of a probability distribution. Compute the expected information gain for selecting a feature.
