Discuss the importance of data : Plotting decision tree in Python

The video tutorial explains how to plot a decision tree in Python. It covers the steps to create a dot file, convert it into an image, and use it to generate a graph. The tutorial provides code and instructions for importing necessary libraries like Pi Dot and using them to build a regression tree. It also explains how to interpret the decision tree, including understanding nodes, variables, and conditions.

What is the first step in plotting a decision tree in Python as mentioned in the video?

• Run the export graph method

• Import the image from Ipython display

• Install the Pi Dot library

• Create a dot file

Which library needs to be installed to visualize a decision tree in Python if it's not already available in Anaconda?

• Scikit-learn

• Pi Dot

• Matplotlib

• Seaborn

What method is used to export the graph of a decision tree?

• draw_tree

• plot_tree

• visualize_tree

• export_graph

In the decision tree, what does a node represent?

• The total number of observations

• The average value of a sample

• The final decision outcome

• A condition to split the data

What information can be found at the leaf nodes of a decision tree?

• The name of the variables

• The conditions for splitting

• The total number of branches

• The average value of each sample