Prediction using Decision Tree Algorithm

Objectives:

• Create the Decision Tree classifier and visualize it graphically.
• If we feed any new data to this classifier, it should be able to predict the right species.

Highlights

How does the Decision Tree model predict species after training?

Let's say we have an unassigned flower with Sepal Length = 6.0, Sepal Width = 4, Petal Lenth = 2.5 and Petal Width = 0.3

Now, we will go down the tree in the following manner until we reach a leaf node:

1. Is the petal_length smaller than or equal to 2.45? No. We can see that it is 2.5. So we will go to the right node.
2. Is the petal_width smaller than or equal to 1.75? Yes. We can see that it is 0.3. So, we will go to the left node.
3. Is the petal_length smaller than or equal to 4.95? Yes. It is 2.5. So, we will go to the left node.

View the Notebook for the full project with a step-wise explanation.