This is a Machine Learning project that implements a Decision Tree Classifier using the concept of postpruning to enhance the efficiency of the model. The project utilizes the popular Iris dataset, which consists of 150 measurements of iris petal and sepal lengths and widths, with 50 measurements for each species of "setosa," "versicolor," and "virginica."
- Efficient Decision Tree Classifier
- Enhanced Accuracy with Postpruning
- Utilizes the Iris Dataset
- Achieves 98% Accuracy
- Install dependencies (Python, scikit-learn).
- Clone the project repository.
- Open the project directory.
- Run the code and see the accuracy results.
Our Decision Tree Classifier with Postpruning achieves an impressive accuracy of 98% on the Iris dataset. This means it can accurately predict the species of an iris flower based on its measurements. The postpruning technique helps prevent overfitting and improves generalization capabilities.