Uploaded bySimplilearn
Decision Tree ID3 Algorithm | Decision Tree | ID3 Algorithm | Machine Learning | 2024| Simplilearn
A decision tree is a supervised learning method for predicting the output of a target variable, effective in both classification and regression problems. It uses the iterative dichotomiser 3 (ID3) algorithm to calculate information gain for features to optimize class separation and build nodes until all features are used or leaf nodes remain. Key concepts include entropy, which measures disorder, and information gain, which indicates the effectiveness of features in classification.
Decision Tree ID3 Algorithm | Decision Tree | ID3 Algorithm | Machine Learning | 2024| Simplilearn
- 2. What is aDecision Tree? A decision tree is a tree-based supervised learning method used to predict the output of a target variable. Decision Tree Determine Odds Classificatio n Problems Regression Problems
- 3. How Does aDecision Tree Work?
- 4. How Does aDecision Tree Work?
- 5. Iterative Dichotomiser 3Example Calculate the Information Gain of each feature. Split the dataset SSS using the feature with the highest Information Gain to best separate the classes. Make a decision tree node using the feature with the maximum Information gain. If all rows belong to the same class, make the current node as a leaf node with the class as its label. Continue the process for the remaining features until all features are used, or the decision tree has only leaf nodes. ID3 Algorithm Steps Covid-19 Infection
- 6. Iterative Dichotomiser 3Example Entropy measures disorder, and in a dataset, it quantifies the disorder within the target feature. Entropy Information Gain measures how much a feature reduces entropy, indicating its effectiveness in classifying the target classes. The feature with the highest Information Gain is chosen as the best. Information Gain
- 7. Iterative Dichotomiser 3Example
- 8. Iterative Dichotomiser 3Example
- 9. Iterative Dichotomiser 3Example
- 10. Iterative Dichotomiser 3Example