How to visualise Decision Tree Model – Multiclass Classification in Python
A Decision Tree is a popular machine learning model that is used for both classification and regression tasks. It works by breaking down a dataset into smaller and smaller subsets, while at the same time an associated decision tree is incrementally developed. One of the key advantages of Decision Trees is that they are easy to understand and interpret, even for people without a technical background.
When building a decision tree for multiclass classification, the tree will have multiple branches that lead to the different classes. The tree starts with a root node, and at each internal node, it makes a decision based on the input features and splits the data into subsets. Each leaf node in the tree represents a class label.
Visualizing a Decision Tree can be a powerful way to understand how the model is making its predictions. In Python, the popular library scikit-learn provides a visualization function for Decision Trees called “plot_tree”. This function can be used to create a graphical representation of a decision tree which can be visualized in the form of pdf, png or jpeg.
The plot_tree function has several parameters that can be used to control the appearance of the tree. For example, you can specify the maximum depth of the tree to be plotted, the font size of the labels, and the colors of the nodes.
Another popular library in python for visualizing decision trees is Graphviz. This library can be used to visualize decision tree models in a more flexible way than the plot_tree function of scikit-learn. It provides a lot of options to customize the appearance and formatting of the tree, and can export the tree in a variety of formats such as PNG, PDF and SVG.
In conclusion, visualizing a Decision Tree model can be a great way to understand how the model is working and making its predictions. Python provides several libraries such as scikit-learn and Graphviz, which make it easy to create beautiful and informative visualizations of Decision Trees. These tools can be especially useful when working with multiclass classification problems, as they make it easy to see how the tree is splitting the data into different classes.
In this Machine Learning Recipe, you will learn: How to visualise Decision Tree Model – Multiclass Classification in Python.
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