This page provides an overview of the Decision Tree model applied to the Iris dataset. The Iris dataset is a classic dataset in machine learning, often used for training algorithms in classification tasks.
Iris Dataset
The Iris dataset contains 150 instances of iris plants. Each instance has 4 features: sepal length, sepal width, petal length, and petal width. The dataset has 3 classes, which are represented by different species of iris plants.
Decision Tree Model
A Decision Tree is a supervised learning model that uses a tree-like model of decisions and their possible consequences, featuring a root node, internal nodes, and leaf nodes. Each node in the tree represents a feature (e.g., sepal length), and each edge represents a decision rule (e.g., sepal length <= 5.1 cm).
Decision Tree Example
Here is an example of a Decision Tree node from the Iris dataset:
- Feature: Sepal Length
- Decision: <= 5.1 cm
- Class: Setosa
This node suggests that if the sepal length is less than or equal to 5.1 cm, the plant is most likely a Setosa.
Usage
The Decision Tree model for the Iris dataset can be used to classify new instances of iris plants based on their features.
More Information
For more information about the Decision Tree model and the Iris dataset, you can read our detailed Machine Learning Tutorial.